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Climate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high‐quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended‐Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended‐Köppen classification methods (Cohen's kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5‐8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%). [ABSTRACT FROM AUTHOR]

Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985–2014 relative to 1955–1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi‐model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30‐yr period is projected to increase for 2070–2099 relative to 1985–2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively. Inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 and especially high for the Amazon and the Tibetan Plateau. Plain Language Summary: Compound climate extremes, such as the events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. Can climate model simulate the historical compound extremes? If yes, how the global compound extremes will change in the future? In this study, we found that the global climate model performs well in simulating temporal changes of warm/wet and warm/dry extremes during the period 1955–2014. With greenhouse gas emissions continuing to increase in the future, compound warm/dry and warm/wet extremes show a continuous increase in frequency in the next few decades, while compound cold/dry and cold/wet extremes are projected to occur less frequently. Key Points: A multi‐model ensemble of CMIP6 models performs well in simulating temporal changes of warm/wet extremesThe inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremesThe projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 [ABSTRACT FROM AUTHOR]

While previous studies have largely focused on anthropogenic warming characterized by surface air temperature, little is known about the behaviors of human‐perceived temperature (HPT), which describe the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity and/or wind speed. Here we adopted an optimal fingerprinting method to compare seasonal mean HPTs in China with those from simulations conducted with multiple climate models participating in the Coupled Model Intercomparison Project Phase 6. We found clear anthropogenic signals in the observational records of changes in both summer and winter HPTs over the period 1971–2020. Moreover, the anthropogenic greenhouse gas influence was robustly detected, with clear separation from natural and anthropogenic aerosol forcings. The anthropogenic greenhouse gas forcing plays the dominant role (>90%) of human‐perceived warming. Urbanization effects contribute slightly and moderately to the estimated trends in summer and winter HPTs, respectively, in addition to the effects of external forcing. Plain Language Summary: Human influences have been identified in the observed warming quantified by surface air temperature (SAT), but SAT alone is inadequate as a metric for human thermal comfort. Here we focus on human‐perceived temperature (HPT), which describes the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity, and/or wind speed. We isolate anthropogenic impacts on the observed increase in summer and winter HPTs in China during 1971–2020 by comparing observations with state‐of‐the‐art climate models. Results show that the influence of anthropogenic greenhouse gas is detected, with clear separation from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most (>90%) of the observed human‐perceived warming. Along with the effects of large‐scale anthropogenic forcing, urbanization effects also have a slight to moderate influence on the estimated trends in summer and winter HPTs. Our work is an early attempt to provide quantitative evidence for the physiological impacts of anthropogenic global warming and local urbanization on human beings. Key Points: The warming is quantified by human‐perceived temperature that considers the joint effects of temperature, humidity and/or wind speedHuman influence could be robustly detected in both summer and winter human‐perceived warmingThe observed increase in human‐perceived temperature is mostly attributed to anthropogenic greenhouse gas increases [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]

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]

The impacts of hot, dry, and compound hot‐dry extremes are significant for societies, economies, and ecosystems worldwide. Such events therefore need to be assessed in the light of anthropogenic climate change so that suitable adaptation measures can be implemented by governments and stakeholders. Here we show a comprehensive analysis of hot, dry, and compound hot‐dry extremes over global land regions using 25 Coupled Model Intercomparison Project Phase 6 models and four future emissions scenarios from 1950 to 2100. Hot, dry, and compound hot‐dry extremes are projected to increase over large parts of the globe by the end of the 21st century. Hot and compound hot‐dry extremes show the most widespread increases and dry extreme changes are sensitive to the index used. Many regional changes depend on the strength of greenhouse‐gas forcing, which highlights the potential to limit the changes with strong mitigation efforts. Plain Language Summary: Heatwaves, drought and their joint occurrences can negatively impact populations, economies, and natural systems worldwide. It is therefore of paramount importance that governments and stakeholders assess the risk from such events and adapt accordingly. In this study we use 25 climate models and four emission scenarios from 1950 to 2100 to assess how hot, dry, and compound hot‐dry extremes are expected to change in the future when compared to current climate conditions. We find that such extremes are projected to increase by the end of the 21st century over large parts of global land areas under the highest‐emission, no‐policy, climate change scenario. Hot and compound hot‐dry extremes show the most widespread increases, whereas dry extreme changes are sensitive and more regionally limited depending on the method by which they are computed. Most of the regional changes in hot, dry, and compound hot‐dry extremes can be reduced with strong climate change mitigation efforts to limit future green‐house gas emissions. Key Points: Hot extremes are projected to increase in frequency and intensity over almost all land areas by the end of the 21st centuryDrought changes depend on measure but increase robustly over central and northern South America, the Mediterranean, and southern AfricaCompound hot and dry extremes are sensitive to the drought measure but projected to increase in most regions globally [ABSTRACT FROM AUTHOR]

Although the Coupled Model Intercomparison Project 6 (CMIP6) protocol provides an experiment to estimate effective radiative forcing (ERF), it is only quantified for few models. We present new estimates of ERF for models participating in CMIP6 by applying the method developed in Fredriksen et al. (2021, https://doi.org/10.1029/2020JD034145), and validate our approach with available fixed‐SST forcing estimates. We estimate ERF for experiments with abrupt changes of CO2, 1% increase of CO2, historical forcings, and future scenarios, and demonstrate that CMIP6 ERF is lower than CMIP5 ERF at the end of the historical period, but grows faster than CMIP5 in the future scenarios, ending up at higher levels than CMIP5 at the end of the 21st century. The simulated radiative efficiency of CO2 has not changed much, suggesting that the larger future increase in CO2 concentrations in CMIP6 compared to CMIP5 is important for explaining the forcing difference. Plain Language Summary: To understand climate model responses, it is useful to separate between the drivers of climate change and their responses. We present new estimates of the drivers, called the effective radiative forcing, for the latest generation of climate models (Coupled Model Intercomparison Project 6 (CMIP6)). This estimates the energy imbalance at the top of the atmosphere and is a measure of human and natural influences on climate. Normally this requires additional climate model experiments to make these estimates, but since these have only been run for a few models, we are here aiming to make the best alternative estimates based on more widely available data, following the method in Fredriksen et al. (2021, https://doi.org/10.1029/2020JD034145). We show that our forcing estimates are growing faster during the 21st century for the new CMIP6 models than for the previous generation of models (CMIP5), and suggest this can be attributed to the higher CO2 concentrations in future scenarios for CMIP6 compared to CMIP5. Key Points: Our method to estimate effective radiative forcing based on common model diagnostics is consistent with Coupled Model Intercomparison Project 6 (CMIP6) fixed‐SST estimatesWe present coherent estimates for abrupt CO2, 1% CO2, historical, and future scenario experimentsForcing estimates for the 21st century grow faster for CMIP6 than for CMIP5 models [ABSTRACT FROM AUTHOR]

Bias correction is a vital technique to correct the climate model outputs for regional studies. Prior bias correction methods such as quantile mapping (QM) may be problematic in correcting extreme values. Herein we proposed a novel bias correction method, that is, the piecewise‐quantile mapping (PQM), by combing piecewise mapping with QM, which implements bias corrections on both extreme and nonextreme data. We compared the PQM method and other six commonly used methods in correcting daily precipitation in the Mekong River Basin (MRB) and the Yangtze River Basin (YRB) from two global climate models (GCMs) (i.e., EC‐Earth3 and MPI‐ESM1‐2‐HR) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results show that the PQM perform better in bias correction pertaining to both the precipitation percentiles and most of the extremes indices. For example, the mean absolute error of R20mm (the number of days when precipitation ≥20 mm) is significantly reduced from 21.64 to 9.03 days and from 10.96 to 5.71 days over MRB and YRB, respectively. In addition, the PQM could capture the spatial distributions of extremes indices reasonably well. The PQM developed in this study provides more accurate bias correction of the GCMs outputs, which will reduce the uncertainty in the subsequent analyses, such as climate change impacts on hydrological and biogeochemical cycles, particularly under extreme conditions. [ABSTRACT FROM AUTHOR]

Global climate change is projected to increase the probability of occurrence and severity of droughts. Increased CO2 concentration drives partial closure of plant stomata and reduces evapotranspiration. However, the impact of reduced evapotranspiration due to CO2 on future droughts characteristics in China is unclear. In this study, we have used the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model simulations (GCMs) under historical period, and four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) to estimate drought. We used a widely applicable drought index (i.e., the self‐calibrating Palmer Drought Severity Index [scPDSI]) to evaluate the historical and future drought conditions, using the Penman–Monteith formula with and without the CO2 concentrations. The results show that the increasing trend of scPDSI slows down when the CO2 effect is considered in all scenarios, especially in Heilongjiang, northern Xinjiang, and the Qinghai–Tibet Plateau. The divergence in the slowdown trend among the different scenarios becomes greater after 2030, with higher emission scenarios implying a greater slowdown in the increasing trend of scPDSI. After considering CO2, drought characteristics such as the drought frequency, intensity, and drought area decrease. Increased CO2 concentration on vegetation physiological processes could mitigate future drought. Therefore, the effects of CO2 on plant physiology should be considered in studies of future drought trends to develop more realistic response and adaptation policies to future drought changes. [ABSTRACT FROM AUTHOR]

Arba'een, a Muslim pilgrimage, is one of the largest annual mass gatherings in the world, with a date fixed according to the lunar calendar. Most pilgrims start their long walking journey from Basra/Najaf toward Karbala (about 70–500 km) and are significantly affected by outdoor weather conditions during this period. Here, based on simulations performed using carefully‐selected climate models, we project that heat stress during the pilgrimage is likely to reach a "dangerous" level, defined according to the US National Weather Service criteria, by the end of this century. Moreover, a significant increase in consecutive occurrence of hot days and hot nights is expected within the coming decades, which may cause a high incidence of heat‐related disorders as the human body may not recover from the daytime heat loads. Our study suggests that sound adaptation measures and stringent mitigation actions must be established to ensure a safe pilgrimage in the future. Plain Language Summary: Arba'een is a religious mass gathering, drawing millions of people to the holy city of Karbala in Iraq annually. When this procession occurs in the sweltering summer, it could cause significant heat‐related morbidity and mortality. Despite the growing risk of heat stress with global warming, less attention has been paid to heat‐related issues during this religious ceremony. Without adequate adaptation and mitigation strategies to climate change, more frequent and severe heatwaves (associated with the consecutive occurrence of hot days and nights) in a warmer world may pose a substantial threat to the Arba'een participants' health. Key Points: Future climate change will likely increase the frequency and intensity of heatwaves during Arba'een at Shiite holy sites in IraqAn increase in mugginess (measured by wet‐bulb temperature) is more pronounced at night than during the day in a warmer climateWe project a rise in consecutive occurrences of hot days and nights, which could threaten human health during the Arba'een event [ABSTRACT FROM AUTHOR]

A warmer climate impacts streamflows and these changes need to be quantified to assess future risk, vulnerability, and to implement efficient adaptation measures. The climate simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which have been the basis of most such assessments over the past decade, are being gradually superseded by the more recent Coupled Model Intercomparison Project Phase 6 (CMIP6). Our study portrays the added value of the CMIP6 ensemble over CMIP5 in a first North America wide comparison using 3,107 catchments. Results show a reduced spread of the CMIP6 ensemble compared to the CMIP5 ensemble for temperature and precipitation projections. In terms of flow indicators, the CMIP6 driven hydrological projections result in a smaller spread of future mean and high flow values, except for mountainous areas. Overall, we assess that the CMIP6 ensemble provides a narrower band of uncertainty of future climate projections, bringing more confidence for hydrological impact studies. Plain Language Summary: Greenhouse gas emissions are causing the climate to warm significantly, which in turn impacts flows in rivers worldwide. To adapt to these changes, it is essential to quantify them and assess future risk and vulnerability. Climate models are the primary tools used to achieve this. The main data set that provides scientists with state‐of‐the‐art climate model simulations is known as the Coupled Model Intercomparison Project (CMIP). The fifth phase of that project (CMIP5) has been used over the past decade in multiple hydrological studies to assess the impacts of climate change on streamflow. The more recent sixth phase (CMIP6) has started to generate projections, which brings the following question: is it necessary to update the hydrological impact studies performed using CMIP5 with the new CMIP6 models? To answer this question, a comparison between CMIP5 and CMIP6 using 3,107 catchments over North America was conducted. Results show that there is less spread in temperature and precipitation projections for CMIP6. This translates into a smaller spread of future mean, high and low flow values, except for mountainous areas. Overall, we assess that using the CMIP6 data set would provide a more concerted range of future climate projections, leading to more confident hydrological impact studies. Key Points: A comparison of hydrological impacts using Coupled Model Intercomparison Project version 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles is performed over 3,107 catchments in North AmericaThe CMIP6 ensembles provide a narrower band of uncertainty for hydrological indicators in the futureIt is recommended to update hydrological impact studies performed using CMIP5 with the CMIP6 ensemble [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]

While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the "best" CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time. Plain Language Summary: The rate of Arctic warming varied in time, while the rate of global warming was nearly constant over the period 1960–2020. This led to a variable Arctic Amplification (AA) defined as a ratio of the Arctic warming trend to mean global warming trend. In addition, the Arctic experienced sudden changes in the rate of warming with step‐like changes in annual mean Arctic Amplification around the years 1986 and 1999. Climate models do not capture the second observed sharp increase of the AA. We suggest that the first AA increase around 1986 is primarily due to external forcing (increasing concentration of carbon dioxide) while the second sharp increase around 1999 is dominated by internal climate variability which current models cannot capture accurately within the time scale of the physical phenomena. Key Points: Annual mean Arctic Amplification (AA) within the period 1970–2020 changed in steep steps around 1986 and 1999. It reached values over 4.0Even those CMIP6 models best at reproducing the AA did not reproduce the second sudden increase near 1999We conjecture that the first AA sharp increase is due to external forcing, while the second is due to internal climate variability [ABSTRACT FROM AUTHOR]

Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These "indicator patterns" vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields. Plain Language Summary: Using machine learning tools called neural networks, we identify patterns of the changing climate within climate model data. Changes in the climate can be identified earlier when detecting patterns within maps of multiple variables and seasons than for single maps alone. By visualizing the patterns learned by the neural networks, we can identify which regions, variables, and seasons are most important for detecting climate change. These patterns offer insight into how climate change is represented in different climate models, and how the patterns of climate change will evolve over time. Key Points: Neural networks and their explainability tools can be harnessed to identify patterns of forced change within combined fieldsCombined fields of input allow for earlier detection of the emergence of a forced climate responseExplainable AI techniques can be used to identify patterns that describe the emergence and evolution of forced climate change [ABSTRACT FROM AUTHOR]

Studies on multivariate drought assessment and the climate change impact on a river basin scale are limited in India. Drought monitoring is a challenging subject due to its dependence on different climatic variables. To overcome this, a copula‐based probabilistic multivariate drought index (MDI) has been developed which simultaneously represents the meteorological, hydrological and agricultural drought phenomenon. The four variate Archimedean copula is used in this study to integrate the precipitation, evapotranspiration, soil moisture and streamflow. Hydrologic variables like evapotranspiration, soil moisture and streamflow were simulated using the Soil and Water Assessment Tool model. The future MDI is also analysed to identify the impact of climate change on drought phenomenon using Coupled Model Intercomparison Project‐6 Global Climate Models under four Socio‐Economic Shared Pathways. Drought characteristics like severity and duration were evaluated to identify the present and future drought events. The precipitation and minimum and maximum temperatures were identified to have increasing tendencies in the future scenarios. Most of the future scenarios showed lower drought duration and severity when compared to the reference period. The drought duration and severity are likely to decrease in the future time scales especially under the high emission scenarios. The present study used a novel approach to examine the drought from various perspectives and the study will be useful for drought mitigation and adaptation strategies over the basin. [ABSTRACT FROM AUTHOR]

The global climate models (GCMs) performances of the recently released Coupled Model Intercomparison Project phase 6 (CMIP6) compared to its predecessor, CMIP5, are evaluated to anticipate the expected changes in climate over Egypt. Thirteen GCMs and their multi‐model ensemble (MME) of both CMIPs were used for this purpose. The future projections were compared for two radiative concentration pathways (RCP 4.5 and 8.5) and two shared socio‐economic pathways (SSP 2–4.5 and 5–8.5). The results revealed improvement in most CMIP6 models in replicating historical rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) climatology over Egypt. The MME of the CMIPs revealed that both could reproduce Egypt's spatial distribution and seasonal climate variability. However, the bias in CMIP5 was higher than that for CMIP6. The uncertainties in simulating seasonal variability of rainfall and temperatures were lower for CMIP6 compared to CMIP5. The future projection of rainfall using CMIP6 MME revealed a higher reduction of precipitation (10–26 mm) in the economically crucial northern region than that estimated using CMIP5 (0–17 mm), compared to 133.5 mm rainfall in the base period. CMIP6 also projected a 0.74–1.63°C more rise in Tmax and Tmin compared to CMIP5 by the end of the century. The study indicates more aggravated scenarios of climate changes in Egypt than anticipated earlier, using the CMIP5 models. Therefore, Egypt needs to streamline the existing adaptation and mitigation measures to account for climate projections. [ABSTRACT FROM AUTHOR]

Understanding polar amplification (PA) and its underlying processes is key to accurately predicting the climate system's response to increasing anthropogenic forcings. We examine the amplified warming in the Arctic and Antarctic in 17 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) against satellite data. Large hemispheric differences in PA strength was found in the CMIP6 models. Changes in surface temperature and strength of PA is closely coupled to changes in albedo. The planetary albedo of Earth (αp) is partitioned into a component associated with surface albedo (defined as surface contribution to planetary albedo, αsurfp ${\alpha }_{\mathit{surf}}^{p}$), and a component associated with atmospheric albedo (atmospheric contribution to planetary albedo, αatmp ${\alpha }_{\mathit{atm}}^{p}$). To assess the hemispheric differences in PA strengths, the relative importance of αsurfp ${\alpha }_{\mathit{surf}}^{p}$ and αatmp ${\alpha }_{\mathit{atm}}^{p}$ were investigated. The surface reflection looks different as seen at the surface (defined as surface albedo, αsurf) compared to αsurfp ${\alpha }_{\mathit{surf}}^{p}$ (as seen at the top of the atmosphere). We find a stronger correlation between surface temperature and αsurf in the Arctic than in the Antarctic, with correlation coefficients of −0.94 and −0.88, respectively. Interestingly, the correlation for surface temperature and αsurfp ${\alpha }_{\mathit{surf}}^{p}$ is stronger in the Antarctic than in the Arctic with correlation coefficients of −0.93 and −0.90, respectively. In the southern high latitudes, albedo changes at the surface are more important than changes in the atmosphere, while the opposite applies in the northern high latitudes. Surface temperature changes in the low‐ and mid‐latitudes are strongly associated with changes in αatmp ${\alpha }_{\mathit{atm}}^{p}$, dominated by changes in cloud properties. Plain Language Summary: The temperature is increasing faster in high latitudes compared to other regions of Earth. This is known as polar amplification (PA). The amount of light reflected from a surface is called albedo. Using 17 climate models and satellite data, this study compares the effects of albedo from surface and atmosphere on PA. We find a large spread in PA among models, with stronger PA in the northern hemisphere than in the southern hemisphere. Surface albedo looks different when looking from the surface compared to when looking from the top of the atmosphere (TOA), because of the effects from the atmosphere when looking from the TOA. We find greater correlation between surface temperature and albedo at the surface in the Arctic than the Antarctic. Interestingly, from the TOA we see greater correlation between surface temperature and surface albedo in the Antarctic than in the Arctic. We also find that in Antarctica, changes in surface albedo as seen from the TOA are more sensitive to changes in surface temperature than changes in atmospheric albedo are, while the opposite applies in the Arctic. These differences may provide part of the answer to why there is greater PA in the northern than in the southern hemisphere. Key Points: A strong correlation between CO2‐induced temperature change and planetary albedo is found in all regionsThere is a large spread in polar amplification between modelsCorrelation between CO2‐induced temperature change and contribution from surface albedo to total planetary albedo is clear in the Antarctic, but less clear in the Arctic [ABSTRACT FROM AUTHOR]

This study evaluated the rainfall historical simulations of 15 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in replicating annual and seasonal rainfall climatology, their temporal variability and trends in Bangladesh for the period 1979–2014, considering ERA5 (ECMWF Reanalysis 5th Generation) reanalysis as the reference dataset. Shannon's Entropy decision‐analysis was employed for GCMs' rating based on eight statistical indicators and a comprehensive rating metric for the final grading of the GCMs. The majority of the CMIP6 GCMs accurately reproduced the spatial feature of ERA5 rainfall. However, the GCMs underestimated annual rainfall by an average of 190.5 mm, with the highest underestimation in monsoon (131.76 mm) and least in winter (3.52 mm) seasons. Most GCMs also underestimated rainfall variability for all seasons except winter. Besides, the GCMs showed an increasing trend in pre‐monsoon and a decreasing trend in post‐monsoon rainfall like ERA5, but an opposite (negative) to ERA5 trend (positive) in monsoon season rainfall. The ensemble mean of the GCMs showed higher skill in reconstructing rainfall climatology, temporal variability and trends than the individual GCMs. The study identified MPI‐ESM1‐2‐LR, MPI‐ESM1‐2‐HR, and GFDL‐ESM4 as the most effective GCMs in reproducing precipitation over Bangladesh. The selected models' simulation can be used for climate change impact assessment in Bangladesh after bias minimization. [ABSTRACT FROM AUTHOR]

While the evidence for anthropogenic climate change continues to strengthen, and concerns about severe weather events are increasing, global projections of regional climate change are still uncertain due to model‐dependent changes in large‐scale atmospheric circulation, including over North Atlantic and Europe. Here, the Jenkinson–Collison classification of daily circulation patterns is used to evaluate past and future changes in their seasonal frequencies over Central Europe for the 1900–2100 period. Three reanalyses and eight global climate models from the Coupled Model Intercomparison Project phase 6, were used based on daily mean sea‐level pressure data. Best agreement in deriving relative frequencies of the synoptic types was found between the reanalyses. Global models can generally capture the interannual variability of circulation patterns and their climatological state, especially for the less frequent synoptic types. Based on historical data and the shared socioeconomic pathway 5 scenario, the evaluated trends show more robust signals during summer, given their lesser internal variability. Increasing frequencies were found for circulation types characterized by weak pressure gradients, mainly at the expense of decreasing frequencies of westerlies. Our findings indicate that given a high‐emission scenario, these signals will likely emerge from past climate variability towards the mid‐21st century for most altered circulation patterns. [ABSTRACT FROM AUTHOR]

There is low confidence in projections of Antarctic sea ice area (SIA), due to deficiencies in climate model sea ice processes. Ensemble regression techniques can help to reduce this uncertainty. We investigate relationships between SIA climatology and 21st century change in the Coupled Model Intercomparison Project, phase 6 (CMIP6) multi‐model ensemble. In summer, under a strong forcing scenario, each model loses the majority of its sea ice. Therefore, models with greater historical SIA exhibit greater reductions, so the observed climatology of SIA strongly constrains projections. Ensemble spread in historical summer SIA is smaller than in CMIP phase 5 (CMIP5), and CMIP6 gives a more robust constraint on future SIA. In winter, by 2100 under a strong forcing scenario, 40% of SIA disappears on average, and ensemble spread in historical mean SIA explains approximately half the spread in projected change. A greater winter ice loss in CMIP6 than CMIP5 is explained by the higher climate sensitivities of some CMIP6 models. Plain Language Summary: Antarctic sea ice area is important for ecosystems, human activity, and for the dynamics of the atmosphere and ocean. However, major climate change assessments such as the Intergovernmental Panel on Climate Change Sixth Assessment Report have placed little confidence in projections of Antarctic sea ice area. This is because climate models struggle to simulate many aspects of sea ice area as observed by satellites. However, not all models should be treated equally. Models with relatively more sea ice area in the recent past project greater losses in the future, especially in summer. This suggests that models with historical sea ice area closest to what we have observed may be more reliable in their future simulations. We use this information in a simple statistical model to show that, firstly, the newest climate models largely show near‐total sea ice loss in summer by the end of the 21st century, which was not universally true for older climate models. Secondly, newer climate models lose more winter sea ice than their predecessors in the same time period, which is related to the known greater global warming response to given greenhouse gas concentrations in the newer models. Key Points: We compare Coupled Model Intercomparison Project (CMIP), phase 5 and the CMIP, phase 6 (CMIP6) projections of Antarctic sea ice loss, assessing drivers of uncertainty and the robustness of projectionsUnder strong forcing, CMIP6 models lose most Antarctic summer sea ice by 2100, so projections are constrained by historical climatologyIncreased winter sea ice loss in CMIP6 is related to increased equilibrium climate sensitivity [ABSTRACT FROM AUTHOR]

The persistent inter‐model spread in the response of global‐mean surface temperature to increased CO2 (known as the "Equilibrium Climate Sensitivity," or "ECS") is a crucial problem across model generations. This work examines the influence of the models' present‐day atmospheric circulation climatologies, and the accompanying climatological cloud radiative effects, in explaining that spread. We analyze the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and find that they simulate a more poleward, and thus more realistic, edge of the Hadley cell (HC) in the Southern Hemisphere than the CMIP5 models, although the climatological shortwave cloud radiative effects are similar in the two generations of models. A few CMIP5 models with extreme equatorward biases in the HC edge exhibited high ECS due to strong Southern midlatitude shortwave cloud radiative warming in response to climate change, suggesting an ECS dependence on HC position. We find that such constraint no longer holds for the CMIP6 models, due to the absence of models with extreme HC climatologies. In spite of this, however, the CMIP6 models show an increased spread in ECS, with more models in the high ECS range. In addition, an improved representation of the climatological jet dynamics does not lead to a new emergent constraint in the CMIP6 models either. [ABSTRACT FROM AUTHOR]

Tropical coral reefs are among the most sensitive ecosystems to climate change and will benefit from the more ambitious aims of the United Nations Framework Convention on Climate Change's Paris Agreement, which proposed to limit global warming to 1.5° rather than 2°C above pre‐industrial levels. Only in the latest Intergovernmental Panel on Climate Change focussed assessment, the Coupled Model Intercomparison Project phase 6 (CMIP6), have climate models been used to investigate the 1.5° warming scenario directly. Here, we combine the most recent model updates from CMIP6 with a semi‐dynamic downscaling to evaluate the difference between the 1.5 and 2°C global warming targets on coral thermal stress metrics for the Great Barrier Reef (GBR). By ~2080, severe bleaching events are expected to occur annually under intensifying emissions (shared socioeconomic pathway SSP5‐8.5). Adherence to 2° warming (SSP1‐2.6) halves this frequency but the main benefit of confining warming to 1.5° (SSP1‐1.9) is that bleaching events are reduced further to 3 events per decade. Attaining low emissions of 1.5° is also paramount to prevent the mean magnitude of thermal stress from stabilizing close to a critical thermal threshold (8 Degree Heating Weeks). Thermal stress under the more pessimistic pathways SSP3‐7.0 and SSP5‐8.5 is three to fourfold higher than the present day, with grave implications for future reef ecosystem health. As global warming continues, our projections also indicate more regional warming in the central and southern GBR than the far north and northern GBR. [ABSTRACT FROM AUTHOR]

The performance of 28 CMIP6 models in simulating the Southeast Asian (SEA) climate is investigated. An evaluation methodology is developed to evaluate spatiotemporal patterns of precipitation, near‐surface temperature, and 850‐hPa wind. Mean annual cycles of temperature and rainfall are assessed separately over Indochina, the Maritime Continent, and the Philippines; and the summer and winter seasons are selected for studying spatial patterns. Within these distinctions, statistics comparing the models to reference data are calculated. The patterns' shape and phase are notably studied, and wind direction differences are assessed specifically over the most major wind flows. We present a scoring procedure allowing us to give each model a unique score reporting on the diverse evaluation aspects, hence establishing a ranking. The process rests on a scoring function growing exponentially with the performance: it favours the models combining good performances according to the largest number of criteria. The assessment highlights EC‐Earth3 and EC‐Earth3‐Veg, which conduct excellent simulations of the regional patterns of rainfall and wind. Their temperature outputs are also reliable, but CNRM‐CM6 ranks first for this variable, while finishing third globally. Local differences are discussed: we emphasize, among other things, the diverse impact of topography on temperature and rainfall, or the frequent deviation of winter winds circulating from the South China Sea to the Java Sea. In addition, a similarity study reveals major difficulties for simulating near‐equatorial annual cycles of temperature and precipitation whereas the summer monsoon spatial rainfall patterns bring out the largest discrepancies. In any event, the top models pointed out by our ranking constitute a high‐performance subset that further climate modelling experiments in SEA can rely on. [ABSTRACT FROM AUTHOR]

During March 2021, large regions of Eastern Australia experienced prolonged heavy rainfall and extensive flooding. The maximum daily mean column integrated water vapor transport (IVT) over Sydney during this event was within the top 0.3% of all days since 1980, and the 10‐day mean IVT was in the top 0.2%. In this study, we have examined the change in frequency of extreme IVT events over Sydney in 16 climate models from the Coupled Model Intercomparison Project 6 under two Shared Socioeconomic Pathways (SSP245 and SSP585). Generalized Extreme Value modeling was used to further analyze the change in frequency of extreme IVT events. We found the probability of long duration high IVT events is projected to increase by 80% at the end of the century, but the future change in IVT is correlated to the rate of global and regional warming in each model. Plain Language Summary: During March 2021, large regions of Eastern Australia experienced prolonged heavy rainfall and extensive flooding. This was associated with strong horizontal water vapor transport over this region that persisted for approximately 10 days. The amount of water vapor transported over Sydney during this event was extreme and within the top 0.3% of all days since 1980. In this study, we used climate models to project how much more often events such as these may occur by the end of the twenty‐first century under two greenhouse gas emission scenarios. We found that the probability of long duration high water vapor transport over Sydney, as in March 2021, may increase by 80%. Key Points: Sydney's March 2021 floods were associated with persistent high water vapor transportThe probability of long‐duration high integrated water vapor transport events should increase by the end of the twenty‐first centuryDifferences in climate sensitivity within the CMIP6 ensemble may increase the spread of moisture flux projections [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]

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5‐8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1‐2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro‐ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5‐8.5. The results highlight that region‐specific breeding efforts are required to allow for a successful adaptation to climate change. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison Phase 6 (CMIP6). TaiESM1 is developed on the basis of the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The new additions include an improved triggering function in the cumulus convection scheme, a revised distribution‐based formula in the cloud fraction scheme, a new aerosol scheme, and a unique scheme for three‐dimensional surface absorption of shortwave radiation that accounts for the influence of complex terrains. In contrast to the majority of model evaluation processes, which focus mainly on the climatological mean, this evaluation focuses on climate variability parameters, including the diurnal rainfall cycle, precipitation extremes, synoptic eddy activity, intraseasonal fluctuation, monsoon evolution, and interannual and multidecadal atmospheric and oceanic teleconnection patterns. A series of intercomparisons between the simulations of TaiESM1 and CMIP6 models and observations indicate that TaiESM1, a participating model in CMIP6, can realistically simulate the observed climate variability at various time scales and are among the leading CMIP6 models in terms of many key climate features. Plain Language Summary: Earth System Models are important tools for understanding how earth system changes and providing information for mitigation and adaption facing future climate changes. Taiwan Earth System Model version 1 (TaiESM1) is developed in the Research Center for Environmental Changes, Academia Sinica, Taiwan, with the goal to join the effort of the international model intercomparison—the Coupled Model Intercomparison Phase 6 (CMIP6). Based on the Community Earth System Model version 1, key improvements of TaiESM1 are in the representation of how raining systems starts, how cloud covers shade, how aerosols form and dissipate, and how topography reflects sunlight. We compare TaiESM1s performance with observations and other CMIP6 models to understand TaiESM1's capability in simulating climate systems and future climate changes. We especially focus on evaluating the climate variability on a wide range of time scales, i.e., how climate systems change on those of within one day, day‐to‐day, seasonal, year‐to‐year, and decades‐to‐decades. Our analysis shows that TaiESM1 can realistically represent the observed climate variability and would help to expand our understandings of the earth system with the global scientific community and society as a whole. Key Points: Climate variability in the historical simulation of Taiwan Earth System Model version 1 (TaiESM1) is evaluatedWhile still subject to several common biases, TaiESM1's performance of mean climate states is among the leading CMIP6 modelsTaiESM1 is capable of realistically simulating key climate features, including the diurnal rainfall, monsoon evolution, and teleconnection [ABSTRACT FROM AUTHOR]

Projections of future drought conditions under climate change are an important step in formulating the long‐term climate adaptation strategies. It is therefore valuable to predict the drought conditions in China following the release of the CMIP6 (the phase six of the Coupled Model Inter‐comparison Project). Thus, based on 20 global climate model simulations from CMIP6, we project China's drought conditions and its socioeconomic impacts using the self‐calibrated Palmer Drought Severity Index (scPDSI). Four scenarios are considered in this analysis: SSP1‐2.6 (the low‐level development scenario), SSP2‐4.5 (the middle‐level development scenario), SSP3‐7.0 (the medium to high‐level development scenario) and SSP5‐8.5 (the high‐level development scenario). Under SSP1‐2.6, we observed wetting trends over large areas of China except the arid region during 2020–2099; however, under SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5, significant drying trends are detected in the humid and temperate semi‐humid region, while in other areas there are significant wetting trends. The projected drought conditions are likely to be severe with more frequent monthly occurrences and higher probability of extreme drying conditions, especially in these humid and temperate semi‐humid regions under SSP3‐7.0 and SSP5‐8.5. Consequently, the population exposure to drought in most climatic regions will increase initially up to 2040s and gradually decrease under all the scenarios except SSP3‐7.0; and the humid region will be a future hotspot where the impact of climate on population exposure to drought will be more significant. The economic exposure to drought will increase over the whole China under all four scenarios, especially in the humid and semi‐humid region. Our results have important implications for future drought projections and provide a scientific evidence for developing climate change adaptation strategies and disaster prevention. [ABSTRACT FROM AUTHOR]

Accelerating climate and land‐use change are rapidly transforming Earth's biodiversity. While there is substantial evidence on the exposure and vulnerability of biodiversity to global change at the species level, the global exposure of intraspecific genetic diversity (GD) is still unknown. Here, we assess the exposure of mitochondrial GD to mid‐21st century climate and land‐use change in terrestrial mammal assemblages at grid‐cell and bioclimatic region scales under alternative narratives of future societal development. We used global predictions of mammal GD distribution based on thousands of georeferenced mitochondrial genes for hundreds of mammal species, the latest generation of global climate models from the ongoing sixth phase of the Coupled Model Intercomparison Project (CMIP6), and global future projections of land‐use prepared for CMIP6. We found that more than 50% of the genetically poorest geographic areas (grid‐cells), primarily distributed in tundra, boreal forests/taiga and temperate bioclimatic regions, will be exposed to mean annual temperature rise that exceeds 2°C compared to the baseline period under all considered future scenarios. We also show that at least 30% of the most genetically rich areas in tropical, subtropical and montane regions will be exposed to an increase of mean annual temperature > 2°C under less optimal scenarios. Genetic diversity in these rich regions is also predicted to be exposed to severe reductions of primary vegetation area and increasing human activities (an average loss of 5–10% of their total area under the less sustainable land‐use scenarios). Our findings reveal a substantial exposure of mammal GD to the combined effects of global climate and land‐use change. Meanwhile the post‐2020 conservation goals are overlooking genetic diversity, our study identifies both genetically poor and highly diverse areas severely exposed to global change, paving the road to better estimate the geography of biodiversity vulnerability to global change. [ABSTRACT FROM AUTHOR]

Simulations from the models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), which represent the most recent generation of climate models, are now available. Understanding the performance of these models in simulating historical climate extremes can provide a basis for producing reliable future climate projections. Here, we assess the simulation of 16 indices of temperature extremes defined by the Expert Team on Climate Change Detection and Indices using results from 24 CMIP6 models as compared with results from CMIP5. Comparisons with observations and reanalyses indicate that the CMIP6 models could capture the spatial patterns and temporal variations of the observed temperature extremes well for some indices, although less well for others. Based on spatial and temporal skill scores, CMIP6 ensemble means were more skillful in simulating absolute and threshold indices of extreme temperature than CMIP5 ensemble means were, but the performances of both the CMIP5 and CMIP6 ensemble means in simulating the spatial patterns for duration and percentile indices were relatively unsatisfactory (spatial skill scores S < 0.3). Furthermore, our results suggest that there have been improvements in spatial pattern skill scores in some individual CMIP6 models relative to CMIP5 model scores for summer days, tropical nights, cold spell duration, and diurnal temperature range. Key Points: The performance of CMIP6 models has been evaluated for simulating global temperature extremesCMIP6 models can capture the spatial patterns and temporal variations of temperature extremes indicesCMIP6 shows improvement over CMIP5 in simulating several absolute and threshold indices of extreme temperature [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]

Climate change projections for the Australian monsoon have been highly uncertain in previous generations of coupled climate models. The new Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble provides an opportunity to address the uncertainty in future projections for northern Australia. We find that the range in Australian monsoon projections from the available CMIP6 ensemble is substantially reduced compared to CMIP5, although models continue to disagree on the magnitude and direction of change. While previous CMIP5 studies identified warming in the western equatorial Pacific as important for Australian monsoon projections, here we show that the western Pacific is not strongly connected to northern Australian precipitation changes in the CMIP6 models. By comparing groups of models based on their future projections, we note that the model‐to‐model differences in Australian monsoon projections are congruent with the zonally averaged precipitation response in the Southern Hemisphere tropics within each model. Key Points: Australian monsoon changes are smaller in the available CMIP6 ensemble than the CMIP5 ensembleCMIP6 models still disagree on changes to Australian monsoon precipitation in a warmer worldAustralian monsoon changes are congruent with the zonal mean precipitation change, more evident in CMIP6 than CMIP5 [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]