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This paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry‐trending and wet‐trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM‐RCM initial‐condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society. Plain Language Summary: To prepare society for the effects of future climate change, we need to know what the future climate will be like. In this paper we explain the method that is used to construct six different scenarios that describe possible future climates of the Netherlands. The scenarios make assumptions about future greenhouse gas emissions, and are based on the outcomes of climate models that simulate the response of the climate to these emissions. The KNMI'23 climate scenarios show that strongly reducing global emissions strongly reduces the expected changes in the climate of the Netherlands. In the scenario in which global emissions continue to rise until 2080, Dutch society will have to adapt to a much stronger increases in heat and precipitation extremes, increased risks of droughts with low river discharge in summer, and increased risk of flooding due to high river discharges in winter. In the coming years the climate scenario data will be used to evaluate what needs to be done to keep the country a safe place for people to live in and to thrive in, under changing climate conditions. Key Points: We present a methodology for the construction of regional climate scenarios using a storyline approach to partition uncertaintyResults from CMIP6 are reconstructed with a GCM‐RCM initial condition ensemble to produce high‐resolution scenario data for end‐usersSix scenario variants cover emission uncertainty (high, moderate, low) and uncertainty in the regional response (dry‐trending, wet‐trending) [ABSTRACT FROM AUTHOR]

The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO2) concentration scenarios used in key climate change modeling experiments. It computes the CO2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5‐8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments. Plain Language Summary: Climate model simulations are increasingly used to understand future trends in the severity and frequency of impactful climate hazards and associated physical and socioeconomic risks. Such simulations describe physical climate change in response to scenarios of greenhouse gases and land use change. However, the scenarios most widely used today as the basis for adaptation planning have no assigned probabilities; rather, they are explicitly intended to span a range of arbitrary climate futures. This reduces the applicability of resulting hazards projections for cost/benefit analysis of adaptation investments. To support risk‐based adaptation decision‐making, this study analyzes five sets of probabilistic carbon dioxide emissions simulated by four Integrated Assessment Models (IAM), along with Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 climate model ensemble results, to estimate the probability of future greenhouse gases concentration scenarios. The results of the five IAMs simulations collated in this work vary, and although they might not be representative of the wider literature, most of them suggest that the high‐emission scenario SSP5‐8.5 becomes unlikely as we reach the second half of the century. Based on lessons learned in this exercise, we propose that new sets of IAM and climate model experiments be jointly designed to better support probabilistic climate change risk assessments. Key Points: Probabilistic Integrated Assessment Models (IAM) simulations of future carbon dioxide emissions are used to evaluate the relative probability of greenhouse gases concentration scenariosJoint coordinated IAM and climate modeling experiments should be designed to enable probabilistic climate change risk assessments [ABSTRACT FROM AUTHOR]

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias‐corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub‐periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade‐offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques. Plain Language Summary: Global Climate Models (GCMs) are useful tools to characterize the historical and future evolution of the Earth's climate and its impacts on water resources. Because these models contain errors and their horizontal resolution is too coarse for local impact assessments, spatial downscaling, and bias correction are required steps. In particular, bias correction methods can be trained and applied using all the available historical data or by splitting the time series (e.g., by season or months). Since there are no guidelines for selecting a temporal stratification (TS), we analyze bias‐corrected GCM outputs obtained using three types of strategies (entire period, seasons, and months) and seven bias‐correction techniques over continental Chile. We show that the choice of BCM and the TS applied can modify the projected precipitation signal and seasonality. We also propose using a simple statistical technique to identify if the TS may be a relevant decision for climate impact assessments for a given climate model. Key Points: The choice of temporal stratification (TS) for GCM bias correction is crucial for removing biases, even for GCMs with good raw seasonalityDifferent temporal stratifications used for GCM bias correction may yield different future seasonalities and signals in projected changesThe linear scaling approach can be used to easily identify GCMs whose projections are sensitive to the choice of TS [ABSTRACT FROM AUTHOR]

Uttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs. Plain Language Summary: Uttar Pradesh is the most populated state in India, with most of the population working in the agriculture sector and having a low income. The state's vulnerability to climate change is high due to inadequate infrastructure and heavy dependence on agriculture. Rice is a crucial crop for the state, but this study shows that climate change will decrease rice yields in the future, especially for irrigated rice, due to higher temperatures and shorter growing seasons. While rainfed rice yields may increase in some regions due to increased rainfall, rice production is expected to decline overall. Key Points: Rice yield (combining irrigated and rainfed) in Uttar Pradesh, India, is projected to decrease in the future for SSP2‐4.5 and SSP5‐8.5With a projected increase in rainfall, rainfed rice yield increases in rainfall deficit zones, and irrigation decreases under both shared socioeconomic pathwaysPlanting in the early season could reduce the amount of yield loss for irrigated rice [ABSTRACT FROM AUTHOR]

Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts. Plain Language Summary: Regional warming associated with climate change is already making droughts worse in many places. This trend is expected to continue as global temperatures increase in response to continued emissions of greenhouse gases. With increasing temperatures, soils are projected to lose water to the atmosphere as it draws more and more moisture from the land surface, increasing drought risk. At the same time, climate change is also projected to shift precipitation patterns. This means it is important to understand how changes in rainfall will cause and end droughts in the future. Using state‐of‐the‐art climate models, we investigate how future warming will impact the influence of precipitation on soil moisture in the Mediterranean and Western North America. Our results suggest that current levels of monthly precipitation will be insufficient to alleviate drought conditions in a warmer world. Key Points: Warming causes declines in soil moisture in the Mediterranean and Western North AmericaThe probability of a moderate soil moisture drought increases during both low and high precipitation anomalies, even at 1°C of warmingThe ability of large positive precipitation anomalies to terminate soil moisture droughts will be substantially reduced in a warmer world [ABSTRACT FROM AUTHOR]

Recent years were characterized by spatially co‐occurring hot and wet extremes around the globe, raising questions about the contribution of human‐induced global warming to the changing likelihoods of such extreme years. To characterize spatially co‐occurring extremes we investigate recent trends in global and regional land area that is concurrently affected by hot or wet annual maxima taking observational uncertainty into account. Observed trends in the land area affected by extreme events are assessed in the context of Earth System Model (ESM) simulations for present‐day and early‐industrial climate conditions in a detection and attribution setting. We compare different reanalysis and station‐based observational products to account for observational data uncertainty. At the global scale, trends of spatially co‐occurring hot or wet annual maxima in all observational products can be explained by ESM simulations driven by historical radiative forcing that accounts for human‐induced changes in the composition of the atmosphere but cannot be explained by ESM simulations that account for an early‐industrial radiative forcing. At the regional scale, trends in spatially co‐occurring hot annual maxima are in general coherent among observational products and can in most cases be attributed to human influence on the climate system. Trends in spatially co‐occurring wet annual maxima show differences in some regions, highlighting the importance of a multi‐dataset approach to overcome observational product dependencies. Despite observational uncertainty, we find robust detection and attribution results for many regions. These results can complement previous assessments on regional exposure to hot and wet events from the new IPCC AR6 report. Plain Language Summary: Recent years were characterized by spatially co‐occurring hot and wet extremes around the globe, leading to questions about the contribution of human‐induced climate change to the occurrence of such extreme years. In this study, we investigate trends in the global and regional land area that is concurrently affected by hot or wet annual maxima. Due to observational uncertainties, we investigate different observation‐based data products for temperature and rainfall. By comparing observational trends in the land area affected by extreme events to respective trends from Earth System Model ensemble simulations accounting for climate conditions of the past decades and early‐industrial climate conditions, we can evaluate their attribution to human‐induced climate change. On the global scale, observed trends of spatially co‐occurring hot or wet annual maxima cannot be simulated by climate models when considering early‐industrial climate conditions, but are captured by climate models considering recent climate conditions. This holds also true for the detection and attribution of regional trends in concurrent land area affected by hot annual maxima. Observational products agree in many regions on the detection and attribution of regional trends in concurrent land area affected by wet annual maxima, but also disagree in some regions. Key Points: Global and regional trends of spatially concurrent hot or wet annual maxima can be attributed to human‐induced climate changeIn some regions observational products show inconsistencies in the sign of detectable trends in concurrent wet annual maximaLarge regional differences in spatial exposure and steady increase in concurrent land area affected by hot or wet annual maxima is detected [ABSTRACT FROM AUTHOR]

Global warming has largely advanced spring vegetation phenology, which has subsequently affected terrestrial carbon and water cycles. However, further shifts in vegetation phenology under future climate change remain unclear. We estimated the start of the growing season (SOS) by applying multiple extraction methods based on the NDVI3g data set, and then parameterized and evaluated 11 spring vegetation phenology models that included chilling, forcing, and the photoperiod. Based on scenario data from three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) derived from eight climate models, future vegetation phenology was predicted using the phenology models. Results showed that all the phenology models performed better than the NULL model (mean of the SOS), with the performance of one‐phase models broadly matching that of two‐phase models, although the best models varied by vegetation type. The spatial pattern of simulated SOS was similar among the models, and it explained >75% of the variation. Based on the mean predicted SOS, we found that spring vegetation phenology will continue to advance under strong warming conditions (SSP245 and SSP585), but that the trend of advance will reverse at around 2060 under the SSP126 scenario. The continued trend in SOS advance is likely related to rapid forcing fulfillment under stronger warming conditions. However, under moderate warming, chilling might be reduced and it might require longer to compensate for higher forcing, which ultimately would result in SOS delay. Our findings highlight that trends will likely change under different warming conditions, potentially causing widespread impact on species interaction, biodiversity, and ecosystem function. Plain Language Summary: Phenological change, which is often seen as an indicator of climate change, has received widespread attention. However, despite this focus, comprehension of how vegetation phenology might alter under different climate change scenarios remains inadequate. We used 11 process‐based phenology models to forecast changes in the start of the growing season (SOS) in the Northern Hemisphere under 3 climate change scenarios (SSP126, SSP245, and SSP585). Results showed that the one‐phase models and the two‐phase models exhibited similar performance, which exceeded that of the NULL model (mean of the SOS). Under the strong warming scenarios (SSP245 and SSP585), spring phenology was projected to continue to advance, whereas under the moderate warming scenario (SSP126), the trend of advance was predicted to reverse at around 2060. Our findings suggest that climate change might alter the competition between species for spring resources, and thereby have widespread impact both on biodiversity and on the structure and function of ecosystems. Key Points: Process‐based models outperform statistical models in forecasting changes in spring vegetation phenology in the Northern HemisphereSimulated start of the growing season explained >75% of the percentage of varianceTrend of advance in the start of the growing season in the Northern Hemisphere will reverse by 2060 under moderate warming conditions [ABSTRACT FROM AUTHOR]

Understanding the response of the Indian summer monsoon (ISM) onset to CO2 forcing is of utmost importance for rain‐fed agriculture and water management. In this study, we utilized an idealized symmetric CO2 removal scenario from the sixth phase of the Coupled Model Intercomparison Project to analyze the reversibility of monsoon onset. The results show that ISM onset is reversible but exhibits strong asymmetry: it undergoes minimal changes during the ramp‐up phase, but experiences rapid postponement as the CO2 begins to decline; Eventually, it is delayed more than 1 week when the CO2 concentration is restored to the initial level. To investigate the possible underlying mechanisms, we decomposed the climate response to CO2 forcing into the fast and slow processes. Notably, it is the enhanced slow response, which is driven by long‐term sea surface temperature (SST) changes, that dominates the asymmetric response of ISM onset. This slow response delays the ISM onset by strengthening near‐surface poleward land‐sea moist static energy contrast, thereby weakening the lower‐tropospheric monsoonal circulation. Based on the atmospheric component model simulations, we found that both the uniform SST change and patterned SST changes in the slow response contribute to the delay of ISM onset, but the latter plays a dominant role. Our results emphasize the importance of thoroughly assessing regional hydrological cycle features when designing the CO2 removal pathways. Plain Language Summary: The 2015 Paris Agreement set a target to limit global warming to 2°C by the end of the 21st century, with a preference for achieving 1.5°C. Meeting this temperature goal requires the atmospheric CO2 concentration to peak in this century and then start declining. It is crucial to understand whether the changes in the regional hydrological cycle can be reversed when we reach the global mean temperature goal. This study focuses on examining the responses of the Indian summer monsoon (ISM) onset to idealized CO2 removal (CDR) forcing, in which the atmospheric CO2 concentration decreases symmetrically after increasing. Under the symmetric CDR pathway, the ISM onset displays reversible yet asymmetric evolution, it significantly delays when the CO2 concentration returns to the pre‐industrial level. This asymmetric response is attributed to the slow process of the climate system driven by long‐term sea surface temperature change, which leads to the delay in ISM onset by weakening the lower‐tropospheric circulation and, consequently, the precipitation. The slow response lags behind the evolution of CO2 concentration and plays a dominant role in the global mean surface temperature change during the ramp‐down phase. Understanding these mechanisms is essential for addressing climate change challenges and the impacts on regional monsoonal systems. Key Points: The idealized symmetric CO2 removal forcing leads to a reversible but asymmetric Indian summer monsoon (ISM) onsetThe delayed onset of ISM during the ramp‐down period is primarily dominated by the asymmetric and lagged slow process, which is associated with the long‐term sea surface temperature changeThe slow process delays the ISM onset by enhancing the near‐surface poleward land‐sea moist static energy contrast, which subsequently weakens the monsoonal circulation [ABSTRACT FROM AUTHOR]

To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science. Plain Language Summary: While temperatures around the world continue to warm due to human‐caused climate change, some areas have observed smaller temperature trends than others. Understanding this regional variability in the rate of warming is important when assessing future projections. One location that has observed less warming is across the United States during their summer season. To evaluate temperature variability in this region using real‐world observations and climate model simulations, we use a statistical method from artificial intelligence called neural networks. The goal of the neural network setup is to learn temperature patterns across the United States and then identify whether climate change effects have exceeded the range of natural variability that has occurred in the past. This is called the timing of emergence (ToE), which is the first year that the effect has clearly appeared. We find that the average United States minimum temperature increase has already emerged in historical records. However, we find no ToE for the average maximum temperature, other than in the Western United States. Another important finding of this study is that by using higher resolution climate model data (i.e., more latitude and longitude points), we find better accuracy in the neural network predictions. Key Points: Forced temperature changes have emerged in observations during summer in the United States as detected by an artificial neural networkIncreasing spatial resolution improves neural network skill for predicting the year of a given summer temperature mapWestern United States land surface climate properties contribute to earlier timing of emergence predictions for the SPEAR climate model [ABSTRACT FROM AUTHOR]

High‐resolution climate change projections are increasingly necessary to inform climate policy and adaptation planning. Downscaling of global climate models (GCMs) is required to simulate the climate at the spatial scale relevant for local impacts. Here, we dynamically downscaled 15 CMIP6 GCMs to a 10 km resolution over Australia using the Conformal Cubic Atmospheric model (CCAM), creating the largest ensemble of high‐resolution downscaled CMIP6 projections for Australia. We compared the host CMIP6 models and downscaled simulations to the Australian Gridded Climate Data (AGCD) observational data and evaluated performance using the Kling‐Gupta efficiency and Perkins skill score. Downscaling improved performance over host GCMs for seasonal temperature and precipitation (10% and 43% respectively), and for annual cycles of temperature and precipitation (6% and 13% respectively). Downscaling also improved the fraction of dry days, reducing the bias for too many low‐rain days. The largest improvements were found in climate extremes, with enhancements to extreme minimum temperatures in all seasons varying from 142% to 201%, and to extreme precipitation of 52% in Austral winter and 47% in summer. The ensemble average integrated skill score improved by 16%. Temperature and precipitation biases were reduced in mountainous and coastal areas. CCAM downscaling outperformed host CMIP6 GCMs at multiple spatial scales and regions—continental Australia, Australian IPCC regions and Queensland's regions—with integrated added value ranging from 9% to 150% and higher over densely populated regions more exposed to climate impacts. This data set will be a valuable resource for understanding future climate changes in Australia. Plain Language Summary: High‐resolution climate models are used for producing climate change projections for assessing regional and local climate change impacts and for adaptation and policy formulation. We completed 15 high‐resolution climate simulations using the Conformal Cubic Atmospheric Model at a 10 km spatial resolution over the Australian continent and surrounds based on global climate models (GCMs) used in the Intergovernmental Panel on Climate Change Assessment Report 6. We evaluated the new high‐resolution data set by comparing it to observations and to their host global model across several regions within Australia. The high‐resolution simulations improved the representation of topography, seasonal temperature and annual cycles of temperature, precipitation, as well as the number of dry days, extreme precipitation and extreme minimum temperatures. The high‐resolution projections show improvement compared to GCMs consistently across spatial scales and regions, being particularly high (150%) over urban areas with high exposure to impacts. This new 10 km data set is the highest resolution, and largest ensemble of the latest climate projections for the Australian continent and surrounds and will be a valuable tool for evaluating future climate change impacts. Key Points: We dynamically downscaled 15 CMIP6 global climate model simulations over Australia to a 10 km spatial resolution using the Conformal Cubic Atmospheric modelTwo new assessment metrics are proposed to assess model performance and added value of downscalingThe integrated added value of downscaling can be as high as 150% over highly populated areas [ABSTRACT FROM AUTHOR]

In the coming decades, coastal flooding will become more frequent due to sea‐level rise and potential changes in storms. To produce global storm surge projections from 1950 to 2050, we force the Global Tide and Surge Model with a ∼25‐km resolution climate model ensemble from the Coupled Model Intercomparison Project Phase 6 High Resolution Model Intercomparison Project (HighResMIP). This is the first time that such a high‐resolution ensemble is used to assess changes in future storm surges across the globe. We validate the present epoch (1985–2014) against the ERA5 climate reanalysis, which shows a good overall agreement. However, there is a clear spatial bias with generally a positive bias in coastal areas along semi‐enclosed seas and negative bias in equatorial regions. Comparing the future epoch (2021–2050) against the historical epoch (1951–1980), we project ensemble‐median changes up to 0.1 (or 20%) in the 1 in 10‐year storm surge levels. These changes are not uniform across the globe with decreases along the coast of Mediterranean and northern Africa and southern Australia and increases along the south coast of Australia and Alaska. There are also increases along (parts) of the coasts of northern Caribbean, eastern Africa, China and the Korean peninsula, but with less agreement among the HighResMIP ensemble. Information resulting from this study can be used to inform broad‐scale assessment of coastal impacts under future climate change. Plain Language Summary: In the next few decades, coastal flooding is expected to become more frequent due to rising sea levels and changes in storms. To understand and prepare for these changes, we use a global hydrodynamic and multiple climate models to investigate how storm surges (a temporarily rise in water level during a storm) will respond to a warmer climate. These are the first global projections of storm surges based on highly detailed climate models (25–50 km). We compare the model output for recent years (1985–2014) with real‐world weather data, known as reanalysis data, and find a good overall agreement. However, the comparison also shows relatively large differences with larger or smaller values in certain areas. For the future (2021–2050), results show storm surge changes up to 20%. These changes vary around the world. Some areas (Mediterranean, northern Africa, and southern Australia) might experience lower storm surges, whereas other areas (South Australia, Alaska, the northern Caribbean, eastern Africa, China, and the Korean Peninsula) might experience higher storm surges. By using advanced computer models as done here, we are able to better understand how climate change could impact coastal area and thus make informed decisions for the future. Key Points: Storm surge projections from 1950 to 2050 based on the Global Tide and Surge Model and a ∼25 km‐resolution High Resolution Model Intercomparison Project climate model ensembleValidation against ERA5 reanalysis (1985–2014) shows that the model performs well globally, but also reveals a clear spatial biasThe median‐ensemble change of the 1 in 10‐year storm surge levels from 2021–2050 compared to 1951–1980 shows changes up to 0.1 m or 20% [ABSTRACT FROM AUTHOR]

Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency. Plain Language Summary: Winter storms can have devastating socioeconomic impacts across the United States. Climate models are used to understand and predict these winter storms and how they might change in the future, therefore helping to inform climate policy and emergency services. It is essential that we evaluate these climate models so that their performance and accuracy can be optimized. One way of evaluating climate models is to assess their ability in reproducing the large‐scale atmospheric conditions which occur during extreme events. Here we apply a cyclone tracking algorithm and self‐organizing maps, a machine‐learning approach, to automate the process of identifying key patterns associated with historical winter storm activity. We then use this approach to assess if CMIP6 climate models are able to reproduce the same patterns including the winter storm frequency and intensity. Our results show that, regardless of resolution, most CMIP6 models struggle to simulate the more extreme patterns and tend to favor weaker patterns that are not as conducive to cyclone formation. As a result, most models simulate too few winter storms, however, the error in storm intensity is more varied, with some models producing stronger storms and others producing weaker storms on average. Key Points: Despite resolution, CMIP6 models struggle to reproduce amplified synoptic patterns over the US, with a robust bias toward zonal patternsMost CMIP6 models produce too few cyclones over the northeastern US, while model errors are more varied when considering cyclone intensityIncreased resolution increases the number and intensity of simulated extratropical cyclones but does not improve model representation of synoptic patterns [ABSTRACT FROM AUTHOR]

In contrast to the "wet gets wetter and dry gets drier" paradigm, here, using observations and climate model simulations, we show that the mean rainfall over the semi‐arid northwest parts of India and Pakistan has increased by 10%–50% during 1901–2015 and is expected to increase by 50%–200% under moderate greenhouse gas (GHG) scenarios. The GHG forcing primarily drives the westward expansion of the Indian summer monsoon rainfall (ISMR) and is facilitated by a westward expansion of the Indian Ocean (IO) warm pool. Mechanistically, the westward expansion of ISMR is a consequence of the episodic genesis over IO and the northward propagation of an expanded Inter‐Tropical Convergence Zone on a sub‐seasonal time scale. While an adaptation strategy to increased hydrological disasters is a must, harnessing the augmented rainfall would lead to a substantial boost in food productivity, bringing transformative changes in the socio‐economic condition of people in the region. Plain Language Summary: An apparent eastward shift of the Indian monsoon has led to the arid conditions in the west and north‐west regions of India where monsoon was once active, and the Indus Valley civilizations thrived (5,300–3,300‐year BP). A reversal of the process and a westward expansion of the present‐day Indian monsoon would transform the west and north‐west India to a humid "monsoonal" climate and provide food security to the expanding population of the country. The present study shows that the Indian monsoon is indeed expanding to the west with 10% decrease of mean rainfall in the northeast and 25% increase in the west and north‐west during the historical period with a potential of 50%–100% increase in the north‐west under SSP2–4.5. Harvesting the increased rainfall has the potential for significant increase in food productivity bringing in transformative changes in the socio‐economic condition of people of the region. Key Points: Global warming could potentially cause a westward expansion of the Indian monsoonRainfall in northwest India and Pakistan has increased and will continue to rise due to greenhouse gasesIncreased monsoon rainfall can improve food productivity and socio‐economic conditions in the region [ABSTRACT FROM AUTHOR]

Most climate models simulate an intensified eastern equatorial Pacific warming in response to increasing greenhouse gas concentrations. So far, the proposed mechanisms have focused on thermodynamic aspects such as the spatial inhomogeneity of evaporative feedbacks, and future increases in upper ocean thermal stratification, partly intensified by projected Walker circulation weakening. Here we show, using earth system model freshwater perturbation experiments, that the simulated future rainfall intensification along the equator plays an important role in tropical climate change. Associated negative equatorial salinity anomalies of about −0.6 permil, which are very similar to the projected late 21st century salinity anomalies in CMIP6 SSP585 scenario, strengthen upper ocean stratification in the Pacific which leads to a flattening of the thermocline, shoaling of the equatorial undercurrent, and major shifts in tropical ocean dynamics. The resulting eastern basin equatorial warming also contributes to a marked weakening of the Pacific Walker circulation. Our analysis illustrates the importance of rainfall and salinity changes in the tropical climate system with relevance for understanding both, patterns of future climate change as well as the El Niño Southern Oscillation phenomenon. Key Points: Future increased tropical rainfall and its effect on salinity contribute to the generation of the enhanced eastern Pacific warming patternSalinity‐driven enhanced eastern Pacific warming weakens Pacific Walker circulationRainfall‐induced thermocline flattening causes a subsurface cold wedge in the western to central tropical Pacific [ABSTRACT FROM AUTHOR]

This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision‐making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution‐based insights can be combined with the latest dynamical models to predict droughts at 8‐month lead‐times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro‐pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization "Early Warning for All" Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limitedfunding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African‐led EWS, and building better links between EWS and agricultural development efforts can support long‐term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross‐silo collaborations. Plain Language Summary: Eastern East Africa is extremely food insecure. Millions of farmers and pastoralists rely on two meager rainy seasons that arrive twice a year. In the 13 seasons since late 2016, the region experienced eight droughts and three exceptionally wet seasons. Seven droughts were linked to very strong Pacific sea surface temperature (SST) gradients, which arose through an interaction between climate change and La Niña. Climate change will bring more extreme Pacific and Indian Ocean SST gradients. Here, for the first time, we show that these gradients can be very well predicted by the current generation of climate models. We then discuss how such information might be used to inform risk management, agriculture, and livestock management practices. The IGAD Climate Predictions and Applications Center, Ethiopian and Kenyan meteorological agencies, and other groups are providing increasingly accurate climate information. This creates opportunities for more proactive and effective agricultural and pastoral advisory services. Trust, urgency and accuracy can lower uncertainty, reduce risk aversion, and empower poor households and cash‐strapped institutions to act and innovate. Investing now in collaborative African climate systems, participatory advisory services and proactive risk management will help counter these threatening climate extremes. Key Points: Climate change and La Niña are producing extreme Pacific sea surface temperature (SST) gradients, which can be predicted very far in advanceThese Pacific SST forecasts provide robust opportunities for predicting well wet and dry outcomes in East AfricaTrust, urgency and accuracy can overcome barriers due to limited funding, uncertain tradeoffs, and inertia by improving advisory services [ABSTRACT FROM AUTHOR]

Current methods for climate change assessment ignore the significant differences in uncertainty in model projections of the two key constituents of drought, precipitation, and evapotranspiration. We present here a new basis for assessing future drought using climate model simulations that addresses this limitation. The new method estimates the Standardized Precipitation Evapotranspiration Index (SPEI) in a two‐stage process. The first stage of our proposed approach is to derive the Standardized Precipitation Index (SPI) using reliable atmospheric variables, which are filtered with a wavelet‐based spectral transformation. This derived SPI is then converted to an equivalent SPEI by combining it with climate model evapotranspiration simulations. We assess the performance of our proposed approach across Australia. The consistency of general circulation model (GCM) drought projections, in terms of both frequency and severity, is improved using the derived SPI. Incorporating evapotranspiration further improves the consistency of the multiple GCMs and drought time scales. The proposed framework can also be generalized to other water resources applications, where the differences in GCM uncertainty for precipitation and evapotranspiration affect climate change impact assessments. Plain Language Summary: Drought is affected by both rainfall and evapotranspiration. Drought indices represent drought severity compared to normal conditions as a function of time. Some drought indices are based on rainfall alone and some use both rainfall and evapotranspiration in the calculations. To understand future drought risk, simulations from climate models are needed. Unfortunately, different climate models often disagree on amounts and patterns of rainfall in the future, the disagreement being considerably more on rainfall than for evapotranspiration. In this study, we attempt to reduce the impact of these differences by developing a new method to estimate future drought. We used a mathematical method known as wavelets to estimate drought indices based on rainfall. Evapotranspiration is then used directly from the climate model and combined with the rainfall based drought index to create one overall drought index. We used projections from multiple climate models to understand if our new method led to a greater agreement in how often and severe future droughts may be. Our results confirm that the new method offers greater consistency in drought projections for the future. Key Points: Current methods for projecting drought ignore differences in uncertainty between P and ET simulationsA new basis for projecting drought is proposed that explicitly accounts for relative uncertainty between P and ETOur results show that the new method offers greater consistency in drought projections for the future [ABSTRACT FROM AUTHOR]

Changes in climate and land‐use and land‐cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (a) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios, (b) sensitivity, and (c) adaptive capacity. The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increases are expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Strong variation in predicted estuarine nutrient loads was observed as a function of climate model projections, while projected LULC changes were more consistently associated with elevated loads. Our findings illustrate the benefits of integrating natural and socio‐ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries. Plain Language Summary: Estuaries provide ecological, economic, and cultural benefits threatened by changes in land‐use and land cover (LULC) and climate. One way in which climate and LULC changes threaten estuaries is by altering the quality and quantity of freshwater they receive. This study developed a framework for identifying estuarine systems at risk of suffering from water quality degradation—specifically through increases in nutrients—due to climate and LULC change. Many factors capturing biological, physical, ecological, and social aspects of estuarine vulnerability to water quality change were analyzed in the framework, which was used to assess the vulnerability of 112 estuarine systems across the contiguous US to future increases in nutrients. Study findings revealed that the largest increases in future estuarine nutrient loads are expected along the Atlantic and eastern Gulf of Mexico, and the lowest increases are expected along the Pacific and western Gulf of Mexico. However, the North Atlantic and the South Pacific were associated with the greatest access to resources for mitigating ecological degradation (e.g., access to local knowledge, legislative and governmental actions), which could help estuaries in these regions better cope with the effects of LULC and climate on nutrient loads, making them potentially less vulnerable. Key Points: A framework to assess water quality vulnerability to future land‐use and climate change was developed and applied to 112 U.S. estuariesSystems along the Atlantic coast had the highest predicted exposure to nutrient loading changeThe North Atlantic and South Pacific regions had the highest adaptive capacity, which can help mitigate the effects of nutrient increase [ABSTRACT FROM AUTHOR]

For many years now, studies on climate change impacts on continental hydrology have suffered from conflicting results between different approaches. On the one hand, studies relying on widely used, simple metrics of land water availability, such as the Aridity Index and Palmer Drought Severity Index, depict predominantly drier future land surface conditions, when driven by climate change projections from global models. On the other hand, the same climate models also generate their own land surface projections, which exhibit balanced changes in land hydrology, with spatially heterogenous changes in soil moisture or runoff. Writing in Earth's Future, Scheff et al. (2022, https://doi.org/10.1029/2022ef002814) provide a comprehensive modeling assessment of the various processes responsible for these contrasted projections, resolving this conflict and thereby improving our understanding of future land hydrology. Key Points: Simple climate‐based metrics and full‐complexity land model simulations yield conflicting results regarding the future of the water cycleScheff et al. (2022) highlight the processes responsible for this discrepancyChanges in plant physiology and in rainfall distribution are key drivers of hydrological changes, which are omitted from simple metrics [ABSTRACT FROM AUTHOR]

Compound dry and hot events (CDHEs) have increased significantly and caused agricultural losses and adverse impacts on human health. It is thus critical to investigate changes in CDHEs and population exposure in responding to climate change. Based on the simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6), future changes in CDHEs and population exposure are estimated under four Shared Socioeconomic Pathways climate scenarios (SSPs) at first. And then the driving forces behind these changes are analyzed and discussed. The results show that the occurrence of CDHEs is expected to increase by larger magnitudes by the end of the 21st century (the 2080s) than that by the mid‐21st century (2050s). Correspondingly, population exposure to CDHEs is expected to increase significantly responding to higher global warming (SSP3‐7.0 and SSP5‐8.5) but is limited to a relatively low level under the modest emission scenarios (SSP1‐2.6). Globally, compared to 1985–2014, the exposure is expected to increase by 8.5 and 7.7 times under SSP3‐7.0 and SSP5‐8.5 scenarios by the 2080s, respectively. Regionally, Sahara has the largest increase in population exposure to CDHEs, followed by the Mediterranean, Northeast America, Central America, Africa, and Central Asia. The contribution of climate change to the increase of exposure is about 75% by the 2080s under the SSP5‐8.5 scenarios, while that of population change is much lower. The conclusion highlights the importance and urgency of implementing mitigation strategies to alleviate the influence of CDHEs on human society. Plain Language Summary: Under global warming, the occurrence of compound dry and hot events (CDHEs) is expected to increase, which may cause more severe damage to human society. Based on the Coupled Model Intercomparison Project Phase 6, the changes in population exposure to CDHEs and the driving forces are estimated and discussed in this study. The results show that the occurrence of CDHEs and population exposure are expected to increase substantially, especially under high‐emission scenarios. Sahara is expected to have the largest increase in population exposure to CDHEs, followed by the Mediterranean, Northeast America, Central America, Africa, and Central Asia. The influence of climate change on the increase in exposure is much larger than that of the population change. Therefore, it is important and urgent to make mitigation strategies to alleviate the impacts of CDHEs on human society. Key Points: The occurrence of summertime compound dry and hot events is projected to increase substantially over the 21st century across the globeExposure is expected to increase significantly under SSP3‐7.0 and SSP5‐8.5 but is limited to a low level under SSP1‐2.6 scenariosClimate change determines the increase in population exposure in most regions of the world [ABSTRACT FROM AUTHOR]

Decomposing the uncertainty of global climate models is highly instructive in understanding climate change. However, it remains unclear whether sources of uncertainty have changed as the models have evolved and the extents to which uncertainty in temperature and precipitation are narrowed after bias correction (BC). We quantified uncertainty in temperature and precipitation projections over global land from three sources—model uncertainty, scenario uncertainty, and internal variability—and compared results from the models participating in the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In addition, we investigated the potential of four BC methods for narrowing uncertainty in temperature and precipitation over the globe and individual continents. Raw projections of temperature and precipitation have greater uncertainty and lower fractional uncertainty relative to their anomalies. The largest temperature uncertainties appear in high‐latitude and high‐altitude regions, and the largest precipitation uncertainties are in low‐latitude regions and mountainous and coastal areas. For uncertainties in CMIP6 temperatures, the contribution from model uncertainty decreases with time (from 99% to 39%), while the contribution from scenario uncertainty increases with time (from 0.01% to 61%). For precipitation projections, the contribution from model uncertainty predominates (98%), while the contributions from scenario uncertainty (1.8%) and internal variability (0.2%) are extremely low. Four BC methods have exhibited excellent ability to reduce uncertainty, and among them, BC and spatial disaggregation has the best performance. These findings can help us better understand the characteristics of the models, while also providing decision makers with more accurate information to address climate mitigation and adaptation measures. Plain Language Summary: Global climate models (GCMs) have a powerful ability to reproduce past climate characteristics and project future climate evolution, and they are currently one of the most effective tools for climate change research. However, quantitative climate projections from GCMs are subject to high uncertainty due to our incomplete knowledge of climate, insufficient representation of climate system, and limited computer resources. Clarifying uncertainty sources can provide important scientific support for enhancing the credibility of future projection results and resolve relevant scientific questions for subsequent modeling applications. In this study, we tried to quantify the uncertainty in temperature and precipitation projections from three sources‐model uncertainty, scenario uncertainty, and internal variability‐arising from model outputs of 21 phase 5 of the Coupled Model Intercomparison Project and 26 phase 6 of the Coupled Model Intercomparison Project GCMs. We also investigated the potential of four bias correction methods for narrowing uncertainty in temperature and precipitation projections over the globe and continents. Key Points: For uncertainties in phase 6 of the Coupled Model Intercomparison Project temperatures, the contribution of model uncertainty decreases with time, while the contribution of scenario uncertainty increases with timeFor precipitation projections, the model uncertainty predominates (98%) the total uncertaintyFour bias correction (BC) methods have exhibited excellent ability to reduce uncertainty, and BC and spatial disaggregation has the best performance [ABSTRACT FROM AUTHOR]

Employing a model democracy in which each model is equally weighted may lead to a poor estimation of the true uncertainty in climate projections from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The improvement and increase in number of CMIP6 models compared with previous phases of CMIP indicate that both model skill and independence need to be considered to provide convincing projections. In this study, we use a weighting scheme, which weights both the skill and independence of multi‐model simulations, to efficiently constrain the large uncertainty in the projection of the timing of an ice‐free Arctic. The uncertainty‐constrained projections of CMIP6 show that the multi‐model spread of the projected first year of an ice‐free Arctic can be reduced by about 29 years under the Shared Socioeconomic Pathway (SSP)3–7.0 scenario (a regional rivalry scenario), indicating a faster tendency to an ice‐free Arctic summer than projections based solely on model democracy. A fossil‐fuel‐based development scenario (i.e., SSP5‐8.5) leads to an ice‐free Arctic before the 2070s (ranging from 2038 to 2071), while an ice‐free Arctic occurs slightly later (by ∼10 years) under the SSP2‐4.5 scenario (i.e., intermediate scenario) and the SSP3‐7.0 scenario, but is inevitable this century. The sustainable development scenario (i.e., SSP1‐2.6) is likely to prevent the occurrence of an ice‐free Arctic. Internal variability strongly affects the projection estimated by the equally weighted ensemble; however, it has a negligible impact on the results obtained by the weighting scheme, thereby indicating that the results of this study are robust and convincing. Plain Language Summary: Climate models are a key tool used by scientists to estimate climate changes in the future. However, the outputs of such models carry inherent uncertainties that can affect the confidence in projections. These uncertainties may not be appropriately estimated by simple and traditional methods that do not adequately consider the model skill or independence (i.e., similarity to other models). In this study, we found that the uncertainties in Arctic sea‐ice projections can be largely reduced if the outputs of climate models are weighted according to their performance and independence. By doing so, we can increase the level of confidence in estimating the year when the Arctic is likely to see its first ice‐free summer (sea‐ice extent below 1 × 106 km2). It is indicated that a sustainable development scenario may avoid an ice‐free Arctic. In contrast, a fossil‐fuel‐based development scenario will lead to an ice‐free Arctic before the 2070s (ranging from 2038 to 2071); and an ice‐free Arctic will occur slightly later (by ∼10 years) under an intermediate or regional rivalry scenario, but is still inevitable this century. Key Points: The performance and interdependence of climate models may lead to a poor estimation of the true uncertainty in climate projectionsA weighting scheme considering both model skill and independence can reduce the spread in the first year of an ice‐free Arctic by ∼29 yearsThe weighted spread indicates that the first year of an ice‐free Arctic is likely to occur during 2040–2072 under the SSP3‐7.0 scenario [ABSTRACT FROM AUTHOR]

Every year, millions of people are at risk due to droughts in South Asia (SA). The likely impacts of droughts are projected to increase with global warming. This study uses the new ensemble mean of 23 global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6), population and gross domestic product (GDP) projections to quantify future changes in increasing drought risks and associated socioeconomic exposure across SA and its subregions under 1.5°C and 2°C of warming. We used two shared socioeconomic pathways (SSPs), SSP2‐4.5 and SSP5‐8.5. The most likely realization copula functions are used to model the joint distribution of drought severity and duration. Simultaneously, changes in the bivariate return period are calculated under a warming climate. The frequency of 50‐year historical droughts (under a bivariate framework) might double across 80% of the SA land area under 1.5°C of warming. Conversely, 12% of SA landmasses may suffer extreme droughts under 2°C of warming. The severe drought episode frequency is expected to increase under 1.5°C (40%–75%) and 2°C (60%–90%) of warming relative to the recent climate. The largest exposure increase is projected in R2 and R4, then R1. Additionally, 75% (65%) of the SA population (GDP) could suffer from increased drought risks under the 1.5°C warmer climate, whereas the additional 0.5°C warming will lead to an unbearable regional situation. Limiting global warming to 1.5°C compared with 2°C can significantly reduce the drought risk influence in SA. These findings can help disaster‐risk managers to adopt climate‐smart management strategies. Plain Language Summary: The world has warmed rapidly since 1970, encouraging efforts to reduce climate change and to stabilize global temperatures to between 1.5°C and 2°C warming targets above preindustrial levels. With these aims, the present study explores the spatiotemporal changes in future drought events and their associated socioeconomic exposure over South Asia (SA) and its subregions under a warmer climate. We find that amplified drought risks are projected to increase over southern and southwestern SA under 1.5°C of warming for the SSP2‐4.5 and SSP5‐8.5 scenarios. As the world continues to warm, some land locations, such as the interior of South India and Pakistan, may experience a temporary emergence of a climate change signal that weakens if the climate stabilizes and the Paris Agreement goals are met. In addition, we also found that the frequency of 50‐year historical droughts might double across 80% of the SA land area under the 1.5°C warming target. In contrast, limiting global warming to 1.5°C instead of 2°C can increase the projected population exposures to increased drought risks by almost half. We believe that the study findings will help disaster‐risk managers adopt climate‐smart policies. Key Points: The frequency of 50‐year historical droughts might double across 80% of South Asia (SA) land area under 1.5°C warmingSouthwestern SA is projected to have the largest increase in exposure compared to the other regionsLimiting global warming to 1.5°C instead of 2°C can increase the projected population exposures to increased drought risks by almost half [ABSTRACT FROM AUTHOR]

Millions of lakes from around the world freeze during winter. These frozen surfaces provide essential ecosystem services that are vital to many northern communities. However, the availability of safe lake ice that is oftentimes required to support these services is under threat from climate change. Here we use a 100‐member ensemble of climate model simulations to investigate changes in the presence of safe lake ice for different recreation and provisioning activities across the Northern Hemisphere. Our projections suggest that the duration of safe ice for recreational purposes will shorten, on average, by 13, 17, and 24 days within a 1.5°C, 2°C, and 3°C warmer world (relative to 1900–1929), respectively. The projected change in the duration of safe ice will be greater in higher latitudes, but with the most densely populated lower‐latitude regions experiencing the greatest percent change. The use of lake ice to support critical transportation infrastructure will also be influenced by future warming through the loss of thicker ice this century. However, our projections suggest that the magnitude of change in the duration of safe ice will differ depending on the ice thickness requirements. For transportation that requires the thickest ice cover, the number of days with safe ice will decline by 90%, 95%, and 99% with 1.5°C, 2°C, and 3°C global warming, respectively. We highlight the need for the development and implementation of adaptation plans to address the imminent loss of critical wintertime transportation infrastructure across the Northern Hemisphere. Key Points: The availability of safe lake ice for recreation will decrease, on average, by 13 and 24 days in response to a global warming of 1.5°C and 3°CThe most densely populated regions across the Northern Hemisphere are projected to experience the greatest percent loss of safe lake iceWithout human intervention, the duration of safe ice for winter ice roads is projected to decline by up to 99% within a 3°C warmer world [ABSTRACT FROM AUTHOR]

Precipitation's temporal and spatial patterns under climate change significantly impact global terrestrial ecology and human social activities. Climate models are essential tools for assessing the impacts of climate change and formulating policies to address climate change. The evaluation results of historical climate model simulations can represent the reliability of their future simulations. This study evaluated the simulation capabilities of 41 historical All‐Forcing monthly precipitation simulations and three integrated models over global land in the Coupled Model Comparison Project Phase 6 (CMIP6). The results show that the simulation capability of global climate models (GCMs) in CMIP6 is highly variable overland around the world. This variability is manifested in two aspects: the spatial variability of the comprehensive simulation ability of each model in different geographical regions and climatic zones of the world and the significant difference in the simulation ability of different models in each region. These GCMs generally overestimate global monthly precipitation over land, with the exception of southeast Asia and tropical rainforest climate (Af), where all models underestimate monthly precipitation. Some GCMS can perform well regionally but poorly on the global scale. One example shows that EC‐Earth3's best capability at Cwc climatic zone, surpassing the integrated model, but failed to rank in the top 10 in 22 of the 29 climate zones. Our results highlight the need to select appropriate models for integration when conducting climate change studies at global and regional scales as a critical factor in studying climate change predictions. Plain Language Summary: This study evaluated 41 monthly precipitation models in Coupled Model Comparison Project Phase 6 from geographic regionalization and global climate classification at the global land scale. Quantile integration was used to get three integration models to participate in the evaluation. Indicators used include correlation coefficient, Taylor skill score, Wasserstein distance, and common indicators of deviation analysis. Conclusions mainly include the performance of integrated models is generally better than that of single models on the global scale. The variation deviations of most indices have similar patterns to the corresponding mean deviations. In different geographic regions and climate types around the world, global climate models that perform better are different. In addition, this research also gives the suitable models in each region of the world and the upper limit of the simulation capability of all models in each area of the world. Key Points: The comprehensive simulation capability of each global climate model (GCM) has spatial variability in different geographical regions and climate zonesThere is no single model can achieve good performance on a global scaleMultiple model ensembles (quantile integration in this study) can significantly improve GCM's applicability on the large scale [ABSTRACT FROM AUTHOR]

Tropical cyclones generate large waves that physically damage coral communities and are commonly cited as a worsening threat to coral reefs under climate change. However, beyond projections of ocean basin‐scale changes in cyclone intensity, the other determinants of future coral reef damage such as cyclone size and duration remain uncertain. Here, we determine the extent to which downscaled cyclones represent observed cyclone characteristics that influence wave damage to Australian coral reef regions. We then investigate mid‐century (2040–2060) and end of century (2080–2100) downscaled tracks to assess whether cyclone characteristics will change with future warming under a high‐emissions scenario. We find that spatial uncertainties in downscaled cyclogenesis and track positions limit estimates of reef damage for individual coral reefs and regions. Further, the models are unable to reproduce the most reef damaging cyclones for any of the regions. The downscaled tracks capture observed cyclone characteristics, such as size, impacting the Great Barrier Reef well, but perform poorly for the Northern Territory, with mixed performance for the Coral Sea and Western Australia. We find no clear evidence that cyclones will cause more damage to Australian coral reef regions in the future, at least based on the climate models and downscaling approach examined here. There is increasing interest in using tropical cyclone projections to assess future coral reef exposure to damaging waves. We recommend caution when interpreting such projections due to large uncertainty in the mechanisms that influence reef damaging tropical cyclone characteristics and how these will change with future warming. Plain Language Summary: Tropical cyclone intensity, size and duration together determine the extent to which their waves damage coral reefs. Increased tropical cyclone intensity with climate change is often cited as evidence that tropical cyclones will cause more damage to coral reefs in the future but changes to size and duration remain uncertain. Here, we determine whether tropical cyclones simulated from climate models can represent the observed tropical cyclone characteristics that are important for estimating wave damage to coral reefs and assess how these characteristics might change in the future. We find that the tropical cyclones simulated from climate models capture the observed cyclone characteristics well for those impacting the Great Barrier Reef (with the exception of the most damaging cyclones) with mixed to poor performance for other regions. The projections of future reef damage are uncertain with some models projecting increases and others decreases. Tropical cyclone projections are used in conservation planning to identify and protect the coral reefs least exposed to future tropical cyclones. However, we find that the simulated tropical cyclone tracks explored here are unlikely to represent future reef damaging tropical cyclone characteristics well if used in conservation decision making. Key Points: Wave damage to coral communities from tropical cyclones depends on their intensity, size and duration while tracking near coral reefsDownscaled tracks capture observed cyclone characteristics impacting the Great Barrier Reef with mixed to poor performance in other regionsProjections vary by region and cyclone characteristic with some models projecting increases and others decreases in future reef damage [ABSTRACT FROM AUTHOR]

Understanding the response of extreme precipitation (EP) at a city scale to global warming is critical to reducing the respective risk of urban flooding. Yet, current knowledge on this issue is limited. Here, focusing on an urban agglomeration in the tropics, Singapore, we reveal that future global warming enhances both frequency and intensity of EP, based on simulations with a state‐of‐the‐art convection‐permitting regional climate model. EP intensification can reach maximum "super" Clausius‐Clapeyron rate (≥ $\ge $+7% per K warming) rate, implying a "new normal" of "extreme events get more extreme," which is consistently for both Representative Concentration Pathways 8.5 and 4.5. The intensification is lower for moderate and light precipitation. Also, global warming was found to dampen the urban effect on EP events. The EP enhancement is attributed to the increased atmospheric moisture and convective inhibition due to enhanced low‐level stratification that delays a convection to develop until it becomes more intense. Plain Language Summary: Understanding the response of extreme precipitation (EP) at a city scale to global warming is critical to reducing the respective risk of urban flooding. Here we show that future global warming will enhance both frequency and intensity of EP, based on simulations with a state‐of‐the‐art convection‐permitting regional climate model for an urban agglomeration in the tropics, Singapore. EP is estimated to intensify much more than moderate and light precipitation, implying a "new normal" of "the extreme events get more extreme." Also, global warming was found to dampen the urban effect on EP events. The EP enhancement is attributed to the increased atmospheric moisture and convective inhibition due to enhanced low‐level stratification that delays a convection to develop until it becomes more intense. Key Points: New normal of "extreme events get more extreme" in future city‐scale precipitation is revealedGlobal warming could modify and even reduce the urban footprint on extreme precipitation (EP) eventsThe intensification of EP can reach the maximum at the "super" Clausius‐Clapeyron (≥ $\ge $+7% per K warming) rate [ABSTRACT FROM AUTHOR]

It sounds straightforward. As the Earth warms due to the increased concentration of greenhouse gases in the atmosphere, global temperatures rise and so heatwaves become warmer as well. This means that a fixed temperature threshold is passed more often: the probability of extreme heat increases. However, land use changes, vegetation change, irrigation, air pollution, and other changes also drive local and regional trends in heatwaves. Sometimes they enhance heatwave intensity, but they can also counteract the effects of climate change, and in some regions, the mechanisms that impact on trends in heatwaves have not yet been fully identified. Climate models simulate heatwaves and the increased intensity and probability of extreme heat reasonably well on large scales. However, changes in annual daily maximum temperatures do not follow global warming over some regions, including the Eastern United States and parts of Asia, reflecting the influence of local drivers as well as natural variability. Also, temperature variability is unrealistic in many models, and can fail standard quality checks. Therefore, reliable attribution and projection of change in heatwaves remain a major scientific challenge in many regions, particularly where the moisture budget is not well simulated, and where land surface changes, changes in short‐lived forcers, and soil moisture interactions are important. Plain Language Summary: Heatwaves are arguably the most deadly weather phenomena. As the Earth warms due to higher concentrations of greenhouse gases, one would expect heatwaves to become worse as well, killing even more people unless they are better protected against the heat. However, it turns out that the world is not so simple and that many other factors also influence heatwaves. Land use changes, irrigation, air pollution, and other changes also drive trends in heatwaves. Some of these cause much larger trends while some have counteracted the climate change‐driven trends up to now. In some regions, the causes of high trends have not yet been identified. Current generation climate models often do not simulate all these mechanisms correctly so will have to be improved before we can more confidently trust their description of past trends and projections of future trends in heatwaves. Key Points: The IPCC AR6 WG1 states the "frequency and intensity of hot extremes have increased"The IPCC notes that the effect of increased greenhouse gas on high temperatures is moderated or amplified at local scales by other factorsConfident quantitative attribution statements of the human influence on heatwaves are limited by our understanding of these local processes [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are essential for investigating climate change, but their coarse scale limits their efficacy for climate adaptation planning at the regional scales where climate impacts manifest. Dynamical downscaling of GCM outputs better resolves regional climate and thus provides improved guidance for climate policy at regional scales. Being expensive to run, downscaling uses a subset of GCMs, necessitating careful GCM selection. This evaluation identifies a suitable subset of CMIP6 GCMs for downscaling over Australia by assessing individual GCMs against three criteria: (a) performance simulating daily climate variable distributions, climate means, extremes, and modes; (b) model independence; and (c) climate change signal diversity. Over Australia, GCMs are generally biased cold (warm) for maximum (minimum) temperature, with larger biases for minimum temperature. GCMs are generally wet biased, especially over the monsoonal north, but dry biased over eastern regions. Most GCMs show larger biases for temperature and precipitation over geographically complex, heavily populated eastern regions, relative to other regions. Evaluations identify a distinct group of 11 GCMs that perform consistently poorly across climate variables, statistics, and timescales with widespread, statistically significant biases, versus 13 GCMs that show consistent adequate‐to‐good performance with substantially reduced errors. Assessment of model independence highlights the lack of independence between several high‐performing GCMs, particularly from allied modeling groups, demonstrating the importance of careful ensemble selection when making selective samples of climate space. Once GCM climate signal diversity is considered, 6–8 mid‐to‐high‐performing, independent GCMs occupy the full range of the future climate space and, thus, are suitable for dynamical downscaling over CORDEX‐Australasia. Key Points: Per‐global climate model (GCM) performances varied markedly, though the best‐performing GCMs simulated consistently well across temperature variables and statisticsAssessment of model independence highlights the lack of independence between several high‐performing GCMsSix to eight mid‐to‐high‐performing, independent GCMs capturing a broad range of the future climate space are identified as suitable for downscaling [ABSTRACT FROM AUTHOR]

The frequency of large‐scale anomalous precipitation events associated with heavy precipitation has been increasing in Japan. However, it is unclear if the increase is due to anthropogenic warming or internal variability. Also, it is challenging to develop an objective methodology to identify anomalous events because of the large variety of anomalous precipitation cases. In this study, we applied a deep learning technique to objectively detect anomalous precipitation events in Japan for both observations and simulations using high‐resolution climate models. The results show that the observed increases in anomalous heavy precipitation events in Western Japan during 1977–2015 were not made only by internal variability but the increases in anthropogenic forcing played an important role. Such events will continue to increase in frequency this century. The increases are attributable to the increasing frequency of tropical cyclones and enhanced frontal rainbands near Japan. These results highlight the mitigation challenge posed by the increasing occurrence of unprecedented precipitation events in the future. Plain Language Summary: The increasing frequency of heavy precipitation events in Japan arose public interest regarding its relevance to ongoing climate changes. However, little was known about how much of the observed increasing trend over the last 40 years can be statistically distinguishable from internal natural variability and attributable to climatic changes induced by human activity. Moreover, it was unclear if any rare precipitation events, other than intense precipitation events, have been increased in Japan. Here, we developed a new deep learning technique—autoencoder—that objectively identifies rare precipitation events. Observations and climate simulations suggest that the frequency of rare precipitation events has been increased in Japan over the last 40 years and attributable to human‐induced climatic changes and will continue to increase in the future. Key Points: A new deep learning technique—autoencoder—was developed to objectively identify anomalous precipitation eventsFrequency of anomalous precipitation events has been increased in Japan and will continue to increase in the futureThe increases are attributable to the anthropogenic forcing through the increased tropical cyclones and enhanced frontal rainbands [ABSTRACT FROM AUTHOR]

Meteorological to hydrological drought propagation has been widely studied to reflect the relationship between these drought categories and better understand drought mechanisms. However, global warming may alter the drought propagation features, which are not fully understood. This study aims to investigate changes in meteorological and hydrological drought conditions, especially their propagation features in 1.5–3.0°C warmer climates for 8,655 watersheds globally. First, the three‐month scale standardized precipitation index and the standardized runoff index are calculated based on the precipitation simulated by the 15 global climate models and the runoff simulated by the four hydrological models, respectively. Drought events are then identified using the run theory, followed by the calculation of drought propagation features (i.e., pooling, lag, and lengthening) for matched meteorological and hydrological droughts. As a result, both meteorological and hydrological drought conditions (i.e., duration and severity) would relieve in warmer climates due to increased precipitation for regions excluding Western North America, South America, the Mediterranean, Southern Africa, East Asia, and Australia. However, the drought conditions would be more severe during drought propagation from meteorological to hydrological droughts over most regions. During drought propagation, the worsening drought conditions over half of the regions would be more serious first and then relieved with the rising temperature. These results indicate that efforts to slow down global warming can suppress the deterioration of drought conditions in the propagation. Plain Language Summary: Droughts can be categorized into meteorological, hydrological, agricultural, and socioeconomic types from different impacted aspects. Investigating meteorological to hydrological drought propagation can reflect the relationship between these two drought categories to understand the drought mechanism better. However, it is not fully understood whether global warming would alter the drought propagation features or not. Therefore, we evaluated the change of meteorological and hydrological drought conditions, especially their propagation features in 1.5 to 3.0°C warmer climates for 8,655 watersheds around the world. Here we show that both meteorological and hydrological drought conditions would relieve in warmer climates due to the increase of precipitation for regions excluding Western North America, South America, the Mediterranean, Southern Africa, East Asia, and Australia. However, the drought conditions would be more severe during drought propagation over most regions in warmer climates. Our findings promote a better understanding of drought propagation changes under global warming. Key Points: Both meteorological and hydrological drought conditions are projected to relieve in warmer climates for around half of global regionsThe duration and severity developing from meteorological to hydrological droughts would be more severe over most regionsThe worsening duration and severity during the propagation over half of the regions would intensify first and then ease in warmer climates [ABSTRACT FROM AUTHOR]

Given the rapidly changing climate, accurate spatiotemporal information on the evolution of extreme rainfall events is required for flood risk assessment and the design of resilient infrastructure. Consequently, various research efforts have focused on investigating the appropriateness of various parametric and non‐parametric approaches in modeling the observed changes in the frequency of extreme rainfall over time. Yet, the assumption of stationarity, or the change of model parameters when accounting for nonstationary rainfall, may magnify estimation uncertainty of rain rates associated with low exceedance probabilities. Moreover, the use of climate model results may yield inconclusive outcomes, given the existence of epistemic uncertainties in the frequency of extreme events developing on smaller spatial scales or over complex terrain. Herein, we employ a parametric approach based on multifractal scaling arguments, along with high‐resolution (4‐km) hourly precipitation estimates covering a 40‐year period over CONUS, to derive Intensity‐Duration‐Frequency curves and investigate the spatiotemporal evolution of extreme rainfall over a wide range of characteristic temporal scales and exceedance probability levels. Considering the robustness of the multifractal models even when fitted to short rainfall records, we uniquely apply the framework to sequential 10‐year segments of data, where the rainfall process can be reasonably assumed stationary. The obtained results reveal that existing infrastructure may be severely impacted by the intensification of precipitation extremes due to climate change, with the observed trends being significantly influenced by the topography and rainfall climatology of each region, while depending on the averaging durations and return periods of interest. Key Points: A robust multifractal scheme is applied to sequential data segments to assess the evolution of Intensity‐Duration‐Frequency curvesThe spatiotemporal evolution of extreme rainfall for various averaging durations and return periods reveals infrastructure vulnerabilitiesThe observed extreme rainfall trends are significantly influenced by local topography and rainfall climatology [ABSTRACT FROM AUTHOR]

Water use in the United States reached its lowest level in 2015 in at least four decades. National trends, however, mask local challenges throughout the U.S. In some places, decreases in surface water use were more than offset by increases in groundwater use, leading to net increases in total withdrawals. Other places have seen increasing rates of water shortages caused by mixes of socioeconomic and climate forces. This study examines recent trends in freshwater use and makes projections in future freshwater use over the next 50 years. Projections are based on socioeconomic and climate scenarios from the Intergovernmental Panel on Climate Change Fifth Assessment Report. Scenarios are paired with five climate models from the downscaled Multivariate Adaptive Constructed Analogs (MACA). We find total consumptive water use will decrease by as much as 8% under the best‐case scenario but increase by as much as 235% under the worst‐case scenario. Results depend on both climate and socioeconomic changes, but because agriculture is the dominant use of water in most regions, climate change impacts overwhelm all other factors under hot and dry futures. For the wetter climate models, water use decreases even under the highest emissions levels and highest population growth rates. Plain Language Summary: This study examines recent trends in water use and makes projections for future water use over the next 50 years. Projections are based on scenarios consistent with international climate assessments and provided for a wide range of climate models. Water use could decrease by as much as 8% under the best‐case scenario but increase by as much as 235% under the worst‐case scenario. Because agriculture is the largest user of water in most regions, impacts of climate change on water use are larger than impacts from non‐climate factors like population growth. Key Points: Dry regions see largest increases in freshwater use, with 8% decreases under best‐case scenarios but 235% increases under worst‐case scenariosDespite efficiency gains, population growth is expected to increase domestic water uses in fast growing regionsBecause agriculture is the dominant use of water in most regions, results vary more by climate model more than by socioeconomic scenario [ABSTRACT FROM AUTHOR]

The hydrological characteristics of a river, including the magnitude and timing of high and low flows, are important determinants of its ecological functioning. Climate change will alter these characteristics, triggering ecological changes in river ecosystems. This study assesses risks of ecological change in 321 major river basins across the globe due to global warming relative to pre-industrial conditions of 1.0, 1.5, 2.0 and 3.0°C. Risks associated with climate-driven changes to high and low flows, relative to baseline (1980-2010; 0.6°C warming), are investigated using simulations from nine global hydrological models forced with climate projections from five global climate models, resulting in an ensemble of 14,445 baseline-scenario members for each warming scenario (9 × 5 × 321). At the globalscale, the likelihood of high risks of significant ecological change in both high and low flows increase with global warming: across all basins there is a medium-high risk of change in high (low) flows in 21.4% (22.4%) of ensemble members for 1.0°C warming, increasing to 61.5% (63.2%) for 3.0°C. Risks are particularly pronounced for low flows at 3.0°C for many rivers in South America, southern Africa, Australia, southern Europe and central and eastern USA. Results suggest that boreal regions are least likely to see significant ecological change due to modified river flows but this may be partly the result of the exclusion of processes such as permafrost dynamics from most global hydrological models. The study highlights the ecological fragility and spatial heterogeneity of the risks that unmitigated climate change poses to global river ecosystems. [ABSTRACT FROM AUTHOR]

Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L‐moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L‐variation, and of L‐skewness and L‐kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value (GEV) distribution coupled with a novel application of the Anderson‐Darling Goodness‐of‐fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within ±10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3‐GC31‐MM model performing well in all three scales when compared to the ground‐based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi‐arid regions. Plain Language Summary: Annual maxima of daily precipitation are widely used to design critical infrastructures such as dams and stormwater networks. Climate change is expected to increase the frequency and intensity of extreme events. Climate model projections offer a glimpse into the future and can help assess potential changes and impacts. It is reasonable to assume that climate models simulating accurately the historical climate may also simulate well the future. Here, we assessed the latest generation of climate models, that is the CMIP6 models, to reproduce the historical annual maximum daily precipitation. We compared simulations and observations of extreme precipitation using advanced summary statistics, novel probability similarity measures, and robust statistical tests. The results indicate that models, in general, reproduce well the behavior of annual maximum precipitation, but biases exist especially in the simultaneous behavior of mean and variance. Shortcomings of CMIP6 models are highlighted in the Arctic, Tropics, arid, and semi‐arid regions. Key Points: Assessment of CMIP6 biases in precipitation extremes using L‐moments, novel probability similarity measures and statistical testsCMIP6 models reproduce fairly well observed annual maximum precipitation but biases exist in the joint behavior of mean and variabilityShortcomings of CMIP6 models are highlighted in the Arctic, Tropics, arid, and semi‐arid regions [ABSTRACT FROM AUTHOR]

Responses of summer precipitation in China to global 1.5°C warming with transient and stabilized pathways are investigated through the analysis of the Common Earth System Model climate simulations. Precipitation increases more significantly in southeast China in the stabilized scenario compared with the transient one. The difference is primarily attributable to dynamic factors related to circulation changes and driven by differences of land‐ocean thermal contrast between the two scenarios. The stabilized warming favors conditions of larger ocean warming and smaller land warming, leads to enhanced meridional temperature gradient and ultimately speeds up the jet stream over East Asia. Ageostrophic wind at the entrance of the accelerated jet strengthens ascending motion and precipitation along the coast of Asia from southeast China to India. The enhanced heating over India further induces a Kelvin‐wave response with an anomalous anticyclone that contributes to the enhanced westward shifted of the western North Pacific Subtropical High. Southerly winds at the west flank of the anticyclone transport moisture northward from tropical oceans, and strengthen precipitation in southeast China. Plain Language Summary: This study deals with the issue of regional precipitation response to 1.5°C global warming in accordance to the Paris Agreement on Climate Change for two different pathways, either transient or stabilized. Compared with the transient warming scenario, summer precipitation increases more significantly in southeast China in the stabilized one. This feature of distinctive precipitation responses, with strong implications for policy making in terms of regional climate mitigation and adaptation, is furthermore explored through a moisture budget analysis. Dynamic effect in relation to changes of atmospheric circulation is found to be the major contributor to the difference. The cause of the significant difference in precipitation in South China is attributed to changes of land‐ocean thermal contrast, leading to a series of atmospheric circulation adjustments. Results presented here satisfactorily addressed, for the first time, the long‐standing issue on global warming, namely, how the atmosphere responds distinctively under the transient and stabilized anthropogenic forcing pathways which show however an identical global warming level. Key Points: The summer precipitation increases more significantly in southeast China in the stabilized warming scenario compared with the transient oneThe dynamic effect in relation to changes of atmospheric circulation constitutes the major factor of distinctive precipitation responsesThe differential meridional thermal gradient related to a contrasted land‐ocean warming leads to dynamic changes [ABSTRACT FROM AUTHOR]

Extreme heat occurred over Alaska in June–July 2019, posing risks to infrastructure, ecosystem, and human health. It is vital to improve our understanding of the causes of such events and the extent to which anthropogenic forcing may alter their likelihood and magnitude. Here, we use multiple large ensembles of climate models, comprising thousands of simulated years, to investigate these issues. Our results suggest that the presence of anthropogenic radiative forcing increased the likelihood of the 2019 extreme heat event by as much as 6%. Further we show the rate of occurrence of such an extreme heat event is likely to substantially increase in the future with increasing levels of atmospheric greenhouse gases. While uncertainty in projected climate risk from model choice leads to a broad range of future extreme heat event probabilities, some models project that with rapidly increasing levels of greenhouse gases the likelihood of such events would exceed 75% by 2090. Plain Language Summary: An extended period of high temperatures across the state of Alaska in June and July, 2019 set multiple temperature records. Here, we examine the extent to which human‐driven climate change played a role in increasing the likelihood of experiencing such an extreme event. Using global climate models, we determine that human‐driven climate change increased the probability of experiencing such high temperatures in 2019, and that the likelihood of similar or more extreme events will increase into the coming century. Key Points: The presence of anthropogenic radiative forcing increased the likelihood of the 2019 extreme heat event by as much as 6%The rate of occurrence of such an extreme heat event is likely to substantially increase in the coming decadesBy the 2060s simulations suggest that such an extreme heat event could occur almost every other year, moving from a rare to a common event [ABSTRACT FROM AUTHOR]

The NARCliM project contributes to the CORDEX initiative for Australasia. The first generation of NARCliM (N1.0) used CMIP3 global climate models (GCMs) and provided near and far future estimates of climate change across Australasia at 50‐km and southeast Australia at 10‐km resolution under a business‐as‐usual climate scenario. However, multiple sets of 20‐year periods in N1.0 did not permit analysis of long‐term, inter‐annual to decadal trends across the 21st century. Feedback on user needs for regional climate information revealed the desire for multiple emission scenarios and use of newer CMIP5 GCMs for dynamical downscaling. These limitations led to development of the second iteration of NARCliM, namely NARCliM1.5 (N1.5). The N1.5 downscaling exercise uses CMIP5 GCMs and is temporally expanded to cover 150 years (1950–2100) for two future Representative Concentration Pathways (RCP4.5 and RCP8.5). N1.5 simulations remain at the 50‐km and 10 km resolutions over the same domains as N1.0, thus producing an expanded and complementary data set for regional climate change. N1.5 simulations substantially improve over N1.0 in capturing the seasonal patterns and magnitudes of precipitation, including improvements in overall bias. Conversely, N1.5 shows similar results to N1.0 for maximum and minimum temperature, with no substantial improvement in overall bias. N1.5 projections project a hotter and drier future relative to N1.0. The combined N1.0 and N1.5 ensemble provides a wider spread of future climates more representative of that found in the full CMIP5 ensemble. Together, N1.0 and N1.5 ensembles provide an improved, more comprehensive data set for studying climate change. Plain Language Summary: The second iteration of New South Wales and Australian Capital Territory Regional Climate Modeling (NARCliM) simulations (N1.5) are described, complementing original NARCliM (N1.0) simulations and expanding the ensemble to 24 members. N1.5 simulations substantially improve over N1.0 simulations in capturing the seasonal patterns and magnitudes of precipitation. N1.5 projections add information to the NARCliM ensemble including the potential for hotter and drier futures than those projected in N1.0. Together, the N1.0 and N1.5 ensembles provide an improved, more comprehensive data set for studying climate change. The varying model performances identified here will help the development of the next generation of regional climate simulations over Australia. Key Points: The second iteration of NARCliM simulations (N1.5) are described complementing original NARCliM simulations (N1.0)N1.5 simulations substantially improve over N1.0 simulations in capturing the seasonal patterns and magnitudes of precipitationN1.5 projections add information to the NARCliM ensemble including the potential for hotter and drier futures than those projected in N1.0 [ABSTRACT FROM AUTHOR]

President Joe Biden's administration is poised to make a significant investment in climate research, including the creation of ARPA‐C (Advanced Research Projects Agency–Climate). This commentary serves to promote discourse on ARPA‐C and the future of government‐led climate research. Here, we propose a revolutionary leap to the ARPA model that incorporates a focus on culture and equity in a unique institutional structure anchored by transdisciplinarity. With ARPA‐C, we argue for a radical rethinking of climate interventions that reconciles and evolves the relationship between social, technical, and environmental systems, by finding integrated pathways to improvement via a high‐risk, creative, and transformative climate action agenda. Without research that can address the network of causal relationships across multiple scales, systems, and nations, programmatic models for climate research and implementation are unlikely to succeed. Key Points: Advanced Research Projects Agency–Climate (ARPA‐C) must be a new transdisciplinary platform designed to contend with the wicked problems of climate changeNo US institution has an operational model, requisite funding, and primary mandate to convene global action in response to climate change [ABSTRACT FROM AUTHOR]

The climate science community is challenged to adopt an actionable risk perspective, which is difficult to align with the traditional focus on model‐based probabilistic climate change projections. Event‐based storylines can provide a way out of this conundrum by putting emphasis on plausibility rather than probability. This links directly to common practices in disaster risk management using "stress‐testing" for emergency preparedness based on events that are conditional on specific and plausible assumptions. Event‐based storylines allow for conditional explanations, without full attribution of every causal factor, which is crucial when some aspects of the latter are complex and highly uncertain. Plain Language Summary: One of today's major challenges is how to use insights and information from climate sciences to inform decision‐making regarding managing risks from climate change, where weather and climate extremes represent a major component of climate‐related risk. So far, climate science has taken a probabilistic approach producing large model ensembles and exploring likely ranges, thereby neglecting low‐likelihood but potentially high‐impact events that pose significant risks to society. Event‐based storylines are emerging as an alternative way to explore future high‐impact events while taking into account aspects of vulnerability and exposure of the considered system with an emphasis on plausibility rather than probability. This concept links directly to common practices in disaster risk management using "stress‐testing" for emergency preparedness based on events that are conditional on specific, but plausible assumptions. When co‐developed by climate scientists and stakeholders, event‐based storylines can be informed by physical climate and impact modeling and can provide a useful way of communicating and assessing climate‐related risk in a specific decision‐making context. Key Points: Event‐based storylines are a way to communicate and assess climate risk taking into account aspects of vulnerability and exposureEvent‐based storylines focus on plausibility rather than probability when looking at high‐impact eventsEvent‐based storylines can provide climate information that feeds directly into a particular decision‐making context [ABSTRACT FROM AUTHOR]

Computational advances have made atmospheric modeling at convection‐permitting (≤4 km) grid spacings increasingly feasible. These simulations hold great promise in the projection of climate change impacts including rainfall and flood extremes. The relatively short model runs that are currently feasible, however, inhibit the assessment of the upper tail of rainfall and flood quantiles using conventional statistical methods. Stochastic storm transposition (SST) and process‐based flood frequency analysis are two approaches that together can help to mitigate this limitation. SST generates large numbers of extreme rainfall scenarios by temporal resampling and geospatial transposition of rainfall fields from relatively short data sets. Coupling SST with process‐based flood frequency analysis enables exploration of flood behavior at a range of spatial and temporal scales. We apply these approaches with outputs of 13‐year simulations of regional climate to examine changes in extreme rainfall and flood quantiles up to the 500‐year recurrence interval in a medium‐sized watershed in the Midwestern United States. Intensification of extreme precipitation across a range of spatial and temporal scales is identified in future climate; changes in flood magnitudes depend on watershed area, with small watersheds exhibiting the greatest increases due to their limited capacity to attenuate flood peaks. Flood seasonality and snowmelt are predicted to be earlier in the year under projected warming, while the most extreme floods continue to occur in early summer. Findings highlight both the potential and limitations of convection‐resolving climate models to help understand possible changes in rainfall and flood frequency across watershed scales. Plain Language Summary: High‐resolution "convection‐permitting" regional climate model simulations hold great promise in projection of climate change impacts including extreme rainfall and flooding. The relatively short (~10‐year) model runs that are currently feasible, however, are insufficient for examining very rare events like 100‐year storms and floods. Meanwhile, existing rainfall and flood data sets have a number of shortcomings that make it difficult to understand how floods have and will continue to change. In this study, we use several novel computer modeling methods to help mitigate these limitations. We apply these methods together with detailed simulations of flood hydrology and high‐resolution regional climate simulation results to examine current and future extreme rainfall and flooding in an agricultural watershed in northeastern Iowa, in the Midwestern United States. Floods there are projected to become more severe, driven by complex seasonal changes in rainfall, temperature, and snow. The magnitude of these changes depends on upstream watershed area. This work demonstrates how cutting‐edge climate and hydrology simulations and methods, together with flood theory and data, can help to predict future changes in flooding. Key Points: Process‐based frequency analysis framework is coupled with convection‐permitting RCM outputs to study rainfall and flood nonstationarityWe examine current and future rainfall and flood quantiles up to the 500‐year recurrence interval in a medium‐sized watershedExtreme rainfall enhancement is identified across scales, while changes in flood hazards are highly dependent on scale and magnitude [ABSTRACT FROM AUTHOR]

We present a new set of global and local sea‐level projections at example tide gauge locations under the RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. Compared to the CMIP5‐based sea‐level projections presented in IPCC AR5, we introduce a number of methodological innovations, including (i) more comprehensive treatment of uncertainties, (ii) direct traceability between global and local projections, and (iii) exploratory extended projections to 2300 based on emulation of individual CMIP5 models. Combining the projections with observed tide gauge records, we explore the contribution to total variance that arises from sea‐level variability, different emissions scenarios, and model uncertainty. For the period out to 2300 we further breakdown the model uncertainty by sea‐level component and consider the dependence on geographic location, time horizon, and emissions scenario. Our analysis highlights the importance of local variability for sea‐level change in the coming decades and the potential value of annual‐to‐decadal predictions of local sea‐level change. Projections to 2300 show a substantial degree of committed sea‐level rise under all emissions scenarios considered and highlight the reduced future risk associated with RCP2.6 and RCP4.5 compared to RCP8.5. Tide gauge locations can show large (> 50%) departures from the global average, in some cases even reversing the sign of the change. While uncertainty in projections of the future Antarctic ice dynamic response tends to dominate post‐2100, we see substantial differences in the breakdown of model variance as a function of location, time scale, and emissions scenario. Key Points: We have developed a new set of global and local sea‐level projections for the 21st century and extended to 2300 that are rooted in CMIP5 climate model simulations, including more comprehensive treatment of uncertainty than previously reported in IPCC AR5Analysis of local sea‐level projections and tide gauge data suggests that local variability will dominate the total variance in sea‐level change for the coming decades at all locations consideredThe extended sea‐level projections highlight the substantial multicentury sea‐level rise commitment under all RCP scenarios and the dependence of modeling uncertainty on geographic location, time horizon, and climate scenario [ABSTRACT FROM AUTHOR]

We analyze projected changes in climate extremes (extreme temperatures and heavy precipitation) in the multimodel ensembles of the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6). The results reveal close similarity between both ensembles in the regional climate sensitivity of the projected multimodel mean changes in climate extremes, that is, their projected changes as a function of global warming. This stands in contrast to widely reported divergences in global (transient and equilibrium) climate sensitivity in the two multimodel ensembles. Some exceptions include higher warming in the South America monsoon region, lower warming in Southern Asia and Central Africa, and higher increases in heavy precipitation in Western Africa and the Sahel region in the CMIP6 ensemble. The multimodel spread in regional climate sensitivity is found to be large in both ensembles. In particular, it contributes more to intermodel spread in projected regional climate extremes compared with the intermodel spread in global climate sensitivity in CMIP6. Our results highlight the need to consider regional climate sensitivity as a distinct feature of Earth system models and a key determinant of projected regional impacts, which is largely independent of the models' response in global climate sensitivity. Plain Language Summary: Many articles analyze and compare global climate sensitivity in climate models, that is, how their global warming differs at a given level of CO2 concentrations. However, global warming is only one quantity affecting impacts. To assess human‐ and ecosystem‐relevant impacts, it is essential to evaluate the regional climate sensitivity of climate models, that is, how their regional climate features differ at a given level of global warming. We analyze here regional climate sensitivity in the new multimodel ensemble that will underlie the conclusions of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This ensemble of model projections is called the "Sixth Coupled Model Intercomparison Project" or CMIP6. We find that differences in regional climate sensitivity between models in CMIP6 often contribute more to the uncertainty of regional extremes projections than the uncertainty in global mean warming between models. Overall, the regional climate sensitivity features in the CMIP6 models' projections ensemble are very similar to those of the prior ensemble (CMIP5), although the model ensembles have been highlighted to differ in their global climate sensitivity over the 21st century. Key Points: Changes in climate extremes as a function of global warming are quasilinear and determine a "regional climate sensitivity" in CMIP5 and CMIP6The regional climate sensitivity of climate extremes is found to be very similar in CMIP5 and CMIP6, unlike global climate sensitivityModel spread in regional climate sensitivity in CMIP6 contributes more to uncertainty of projected extremes than global climate sensitivity [ABSTRACT FROM AUTHOR]

The quantification of climate change impacts on hydrology is subjected to multiple uncertainty sources. Large ensembles of hydrological simulations based on multimodel ensembles (MMEs) have been commonly applied to represent overall uncertainty of hydrological impacts. However, as increasing numbers of global climate models (GCMs) are being developed, how many GCMs in MMEs are sufficient to characterize overall uncertainty is not clear. Therefore, this study investigates the influences of GCM quantity on quantifying overall uncertainty and uncertainty contributions of multiple sources in hydrological impacts. Large ensembles of hydrological simulations are obtained through the permutation of 3 greenhouse gas emission scenarios, 22 GCMs, 6 downscaling techniques, 5 hydrological models (HMs), and 5 sets of HM parameters, which enables to decompose uncertainty components using analysis of variance. The influences of GCM quantity are investigated by repeatedly conducting uncertainty decomposition for hydrological simulations from subsets with different numbers of GCMs. The results show that GCMs are the leading uncertainty sources in evaluating changes in annual and peak streamflows, while for changes in low flow, other uncertainty sources except HM parameters also have large contributions to overall uncertainty. Furthermore, on the condition of using no more than five GCMs, there are large possibilities that the overall uncertainty and GCMs' uncertainty contribution are underestimated. Using around 10 GCMs can ensure that the median of different combinations generates similar uncertainty components as the whole ensemble. Therefore, it is recommended to use at least 10 GCMs in studies of climate change impacts on hydrology to thoroughly quantify uncertainty. Plain Language Summary: Simulating hydrological responses to climate change involves multiple steps, which contribute uncertainty. Although climate model simulations are often found to be one of the major sources of uncertainty, how many simulations are needed to thoroughly estimate the uncertainty of hydrological responses is still not clear. Facing the growing number of climate simulations, using all of them will also pose a large computational burden in studying hydrological impacts of climate change. Therefore, we proposed a framework to evaluate how the numbers of climate simulations affect the quantification of uncertainty. Our results show that using insufficient numbers of global climate models leads to the underestimation of uncertainty. It is recommended to use at least 10 global climate models to adequately consider the uncertainty resulting from climate models. Our findings are helpful in the selection of sufficient climate simulations in simulating hydrological responses under climate change. Key Points: A framework to quantify the uncertainty contribution of GCMs over multiple sources in hydrological impacts of climate change is proposedUsing an insufficient number of GCMs may result in the underestimation of overall uncertainty and GCMs' contribution to uncertaintyIt is recommended to use at least 10 GCMs in studies of hydrological climate change impacts to thoroughly quantify uncertainty [ABSTRACT FROM AUTHOR]

Investigation of the pressing impacts of climate change on drought is vital for sustainable societal and ecosystem functioning. The magnitude of how much the drought will change and the way how droughts would affect society and the environment are inadequately addressed over East Africa. This study aimed at assessing future drought changes using an ensemble of five Global Climate Models (GCMs) in the Coupled Model Intercomparison Project (CMIP5) over East Africa. To this end, drought characteristics were investigated under the Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 in the near term (the 2020s; 2011–2040), midcentury (2050s; 2041–2070), and end of century (2080s; 2071–2,100). The changes of the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI) were first compared, and the SPEI was used for measuring future droughts as it showed stronger changes due to its inclusion of temperature effects. Drought area in East Africa is likely to increase at the end of the 21st century by 16%, 36%, and 54% under RCP 2.6, 4.5, and 8.5, respectively, with the areas affected by extreme drought increasing more rapidly than severe and moderate droughts. Spatially, drought event, duration, frequency and intensity would increase in Sudan, Tanzania, Somalia, and South Sudan, but generally decrease in Kenya, Uganda, and Ethiopian highlands. Results also confirm that drought changes over East Africa follow the "dry gets drier and wet gets wetter" paradigm. The findings provide important guidance for improving identification of causes, minimizing the impacts and enhancing the resilience to droughts in East Africa. Key Points: We examined the impact of changes in precipitation and temperature on drought characteristics over East Africa during 1981–2099Precipitation and temperature will increase over East Africa; however, the temperature impact is dominant leading to extreme droughtsDrought area in East Africa is likely to increase at the end of the 21st century by 16%, 37%, and 54% under RCP 2.6, 4.5, and 8.5, respectively [ABSTRACT FROM AUTHOR]

Outputs from new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) promise improvement and enhancement of climate change projections information for Australia. Here we focus on three key aspects of CMIP6: what is new in these models, how the available CMIP6 models evaluate compared to CMIP5, and their projections of the future Australian climate compared to CMIP5 focussing on the highest emissions scenario. The CMIP6 ensemble has several new features of relevance to policymakers and others, for example, the integrated matrix of socioeconomic and concentration pathways. The CMIP6 models show incremental improvements in the simulation of the climate in the Australian region, including a reduced equatorial Pacific cold tongue bias, slightly improved rainfall teleconnections with large‐scale climate drivers, improved representation of atmosphere and ocean extreme heat events, as well as dynamic sea level. However, important regional biases remain, evident in the excessive rainfall over the Maritime Continent and rainfall pattern biases in the nearby tropical convergence zones. Projections of Australian temperature and rainfall from the available CMIP6 ensemble broadly agree with those from CMIP5, except for a group of CMIP6 models with higher climate sensitivity and greater warming and increase in some extremes after 2050. CMIP6 rainfall projections are similar to CMIP5, but the ensemble examined has a narrower range of rainfall change in austral summer in Northern Australia and austral winter in Southern Australia. Overall, future national projections are likely to be similar to previous versions but perhaps with some areas of improved confidence and clarity. Key Points: CMIP6 shows incremental improvement of the simulation of Australian climate compared to CMIP5CMIP6 climate projections are similar to CMIP5 except for a group of higher sensitivity models with warmer projected change [ABSTRACT FROM AUTHOR]

State and local governments, businesses, community organizations, and the general public are taking an increasingly significant role in climate impact assessment. Driven by impacts to constituents and customers and threats to human health, essential services, and property values, local organizations are on the front lines of responding to climate change. National and international efforts such as the U.S. National Climate Assessment and Intergovernmental Panel on Climate Change provide fundamental scientific understanding as well as methods and modeling tools. Subnational climate assessments can build on this foundation and tailor models and analyses to specific local or decision contexts. However, subnational climate assessment and adaptation presents new scientific and research challenges, such as those related to downscaling climate models, simulating extreme events, and understanding local values and institutional practices. As state, local, and sectoral assessments become more common across the nation, there is a critical opportunity to share learnings and identify challenges and pitfalls. Building off a National Academies of Sciences, Engineering, and Medicine activity focusing exclusively on subnational climate assessment, we consider the methods and results from a variety of examples to synthesize findings about current practices and the future of subnational climate assessment. Plain Language Summary: Climate assessments provide descriptions of the current and future impacts of climate change. Such assessments have long been a part of national and international activities on climate change. Increasingly, state and local governments, businesses, and other groups have begun climate assessments that address possible impacts from climate change specific to their constituents or customers. When taking national and international efforts to a local level, two important challenges arise. One is related to the challenge of applying science and modeling developed for the large scale to a smaller scale. The second is the need to translate technical analysis into usable information for decision makers and the public. Here we build off of a workshop convened by the National Academies of Sciences, Engineering, and Medicine to consider these issues. Key Points: State and local governments and others are taking an increasingly significant role in climate impact assessmentSuch assessments present analytical challenges from downscaling national models, simulating extremes, and integrating local perspectivesThe evolution of California's assessments, in its fourth iteration, reflects the demands for climate assessments to translate science into response activities [ABSTRACT FROM AUTHOR]

Recent fire seasons have fueled intense speculation regarding the effect of anthropogenic climate change on wildfire in western North America and especially in California. During 1972–2018, California experienced a fivefold increase in annual burned area, mainly due to more than an eightfold increase in summer forest‐fire extent. Increased summer forest‐fire area very likely occurred due to increased atmospheric aridity caused by warming. Since the early 1970s, warm‐season days warmed by approximately 1.4 °C as part of a centennial warming trend, significantly increasing the atmospheric vapor pressure deficit (VPD). These trends are consistent with anthropogenic trends simulated by climate models. The response of summer forest‐fire area to VPD is exponential, meaning that warming has grown increasingly impactful. Robust interannual relationships between VPD and summer forest‐fire area strongly suggest that nearly all of the increase in summer forest‐fire area during 1972–2018 was driven by increased VPD. Climate change effects on summer wildfire were less evident in nonforested lands. In fall, wind events and delayed onset of winter precipitation are the dominant promoters of wildfire. While these variables did not change much over the past century, background warming and consequent fuel drying is increasingly enhancing the potential for large fall wildfires. Among the many processes important to California's diverse fire regimes, warming‐driven fuel drying is the clearest link between anthropogenic climate change and increased California wildfire activity to date. Plain Language Summary: Since the early 1970s, California's annual wildfire extent increased fivefold, punctuated by extremely large and destructive wildfires in 2017 and 2018. This trend was mainly due to an eightfold increase in summertime forest‐fire area and was very likely driven by drying of fuels promoted by human‐induced warming. Warming effects were also apparent in the fall by enhancing the odds that fuels are dry when strong fall wind events occur. The ability of dry fuels to promote large fires is nonlinear, which has allowed warming to become increasingly impactful. Human‐caused warming has already significantly enhanced wildfire activity in California, particularly in the forests of the Sierra Nevada and North Coast, and will likely continue to do so in the coming decades. Key Points: Annual burned area in California increased fivefold during 1972–2018, mainly due to summer forest fireAnthropogenic warming very likely increased summer forest fire by drying fuels; this trend is likely to continueLarge fall fires are likely to become increasingly frequent with continued warming and possibly gradual declines in fall precipitation [ABSTRACT FROM AUTHOR]

Amplified regional warming suggests that some regions suffer more from global warming. However, present knowledge is still not enough to understand this paramount aspect of global warming. By using climate model simulations, we first highlight that regional warming amplifications directly following anthropogenic emission would torment many undeveloped countries. Then, we find that cloud has the largest contribution to polar amplification, rather than the frequently blamed sea ice decline. The summertime sea ice decline plays the second role in generating strong wintertime polar amplification via lagged energy release. Our findings also propose that heat uptake by the deeper ocean greatly favors the terrestrial amplification. Additionally, the cloud also shows its importance in helping dryland amplification. Our findings suggest that these robust regional warming amplifications are very likely inevitable under anthropogenic emission, therefore strictest global mitigation is required to prevent unacceptable warming over the high risk regions. Plain Language Summary: Amplified regional warming points out that some regions are more suffering under global warming. Thus, we performed this study to figure out robust regional warming amplifications directly following the anthropogenic greenhouse gas emission. We addressed the influences of these regional warming amplifications to human society, such an amplified dryland warming (relative to humid regions) has greater influences on some undeveloped countries. Furthermore, we achieved some new scientific findings that are crucial to understanding causes of amplified regional warming, such as cloud's dominant role on amplified pole warming (relative to lower‐latitudes) rather than the frequently blamed sea ice decline. We also found the important role of deeper ocean heat uptake and cloud in helping amplified land warming (relative to ocean) and amplified dryland warming, respectively. Taken together, our study greatly helps to understand human influences on regional climate change. Key Points: Human‐induced global warming has greater influences on some undeveloped countriesCloud has the largest contribution to polar amplification, rather than the frequently blamed sea ice declineHeat uptake by the deeper ocean greatly favors the terrestrial amplification [ABSTRACT FROM AUTHOR]

Selecting appropriate indicators is essential to aggregate the information provided by climate model outputs into a manageable set of relevant metrics on which assessments of climate engineering ( CE) can be based. From all the variables potentially available from climate models, indicators need to be selected that are able to inform scientists and society on the development of the Earth system under CE, as well as on possible impacts and side effects of various ways of deploying CE or not. However, the indicators used so far have been largely identical to those used in climate change assessments and do not visibly reflect the fact that indicators for assessing CE (and thus the metrics composed of these indicators) may be different from those used to assess global warming. Until now, there has been little dedicated effort to identifying specific indicators and metrics for assessing CE. We here propose that such an effort should be facilitated by a more decision-oriented approach and an iterative procedure in close interaction between academia, decision makers, and stakeholders. Specifically, synergies and trade-offs between social objectives reflected by individual indicators, as well as decision-relevant uncertainties should be considered in the development of metrics, so that society can take informed decisions about climate policy measures under the impression of the options available, their likely effects and side effects, and the quality of the underlying knowledge base. [ABSTRACT FROM AUTHOR]