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Compound climate extremes (here referred to compound dry–hot events and compound pluvial–hot events) result in devastating disasters which threaten water‐food‐energy security. However, in a warming scenario, the risk of occurrence, the quantification of uncertainty, and associated drivers of compound climate extremes—particularly compound pluvial–hot events—have not been fully explored. By leveraging climate model large ensembles, it is revealed that the risk of compound climate extremes is projected to increase 2–3 times over most global land masses in future Representative Concentration Pathway (RCP) 8.5 forcing compared with historical forcing. Increased risks of compound climate extremes are mainly attributed to the changes in temperature and changes in dependence between precipitation and temperature, while the change in precipitation contributing to risk of these two compound climate extremes exhibits approximately spatial complementary. In the warming world, the hot spots of compound dry–hot extremes mainly lie in Europe, South Africa, and the Amazon, while those of compound pluvial–hot extremes mostly lie in the eastern USA, eastern and southern Asia, Australia, and central Africa. These findings help stakeholders and decision makers develop a package of climate change adaptation strategies to manage and mitigate the risk of compound climate extremes. Plain Language Summary: Compound climate extremes can be disastrous for water‐food‐energy security. The risk evaluation and quantification of compound climate extremes have emerged as a critical knowledge gap. Here, using climate model large ensembles, this study demonstrates that the risk of compound climate extremes is projected to increase double to triple times over most global land masses in future Representative Concentration Pathway (RCP) 8.5 forcing compared with historical forcing. Given the increasing risk of compound pluvial–hot extremes in a warming climate being larger than compound dry–hot extremes, compound pluvial–hot extremes should also receive attention. In the future, compound pluvial–hot extremes became more intense over the eastern USA, eastern and southern Asia, Australia, and central Africa. It should be mentioned that the risk evaluation and quantification of compound climate extremes in the shorter time scales should also receive attention. Key Points: Future occurrences of compound climate extremes are remarkably increasingFuture risks of compound pluvial–hot extremes are larger than those of compound dry–hot extremesIncreased risks of compound climate extremes are attributed to changes in temperature and dependence between precipitation and temperature [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]

This study explored the human influence on the observed changes in precipitation extremes across China for the period of 1961–2014 by the latest simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Changes in observations and CMIP6 simulations under different external forcings for annual total wet‐day precipitation (PRCPTOT), the number of heavy precipitation days (R10 mm), very wet days (R95p), extremely wet days (R99p), the maximum 1‐day precipitation amount (RX1day), and the maximum 5‐day precipitation amount (RX5day) are compared using a regularized optimal fingerprinting method. The results show that both anthropogenic (ANT) and greenhouse gas (GHG) experiments generally exhibit increasing trends in precipitation extremes over China, agreeing with the overall observed increases. The ANT influence is robustly detected for R95p, R99p, RX1day, and RX5day over China, with clear separation from the natural forcing. Additionally, the GHG signal is also detectable for R95p, R99p, and RX1day but separated from the natural and other ANT forcings for only R99p and RX1day and from the natural and ANT aerosol forcings for only RX5day. Thus, the intensified precipitation extremes can be attributed to human influence dominated by the GHG effect. However, detections fail for PRCPTOT and R10 mm. The ANT aerosol forcing may partly offset the role of the GHG responses to observed increases despite being undetectable. Based on the detection and attribution results, the observation‐constrained projections indicate that precipitation extremes are projected to significantly increase under the SSP2–4.5 and SSP5–8.5 scenarios but with a smaller magnitude compared to the raw CMIP6 outputs. Plain Language Summary: The human contribution to precipitation extremes across China was analyzed in this study. The observed precipitation extremes generally increase over China during the period of 1961–2014. The changes in human‐induced precipitation extremes are roughly consistent with the observed changes. Based on a detection technique, the human influence can be identified in the changes in the precipitation extremes, which is separable from the role of solar and volcanic activities. The effects of anthropogenic greenhouse gases can also be detected in the observed increases in precipitation extremes over China. Thus, human influence can contribute to intensified precipitation extremes, and greenhouse gas emissions are the dominant contributor. However, precipitation extremes induced by anthropogenic aerosols show decreases across China, and anthropogenic aerosols may make an opposite contribution to the increases in precipitation extremes despite failed detection. The results further show that precipitation extremes over China will significantly increase in the future with a smaller amplitude than those expected from the global climate models in this study. Key Points: Anthropogenic influence, dominated by greenhouse gas emissions, is detectable in intensified precipitation extremes over ChinaAnthropogenic aerosols may partially offset the role of greenhouse gases in the observed increasesThe observation‐constrained projections show smaller increases for precipitation extremes than the raw CMIP6 outputs [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]

Future changes to the hydrological cycle are projected in a warming world, and any shifts in drought risk may prove extremely consequential for natural and human systems. In addition to long‐term moistening, drying, or warming trends, perturbations to the annual cycle of regional hydroclimate variables may also have substantial impacts. We analyze projected changes in several hydroclimate variables across the continental United States, along with shifts in the amplitude and phase of their annual cycles. We find that even in regions where no robust change in the annual mean is expected, coherent changes to the annual cycle are projected. In particular, we identify robust regional phase shifts toward earlier arrival of peak evaporation in the northern regions, and peak runoff and total soil moisture in the western regions. Changes in the amplitude of the annual cycle of total and surface soil moisture are also projected, and reflect changes to the annual cycle in surface water supply and demand. Whether changes become detectable above the background noise of internal variability depends strongly on the future scenario considered, and significant changes to the annual cycle are largely avoided in the lowest‐forcing scenario. Plain Language Summary: State‐of‐the‐art climate models project substantial changes to the climate of the continental United States (CONUS). Models project changes to the yearly average rainfall, soil moisture, and runoff in many regions. But crucially, they also project changes to the annual cycle, with peaks in evaporation, runoff, and soil moisture projected to shift earlier in the year even if the yearly averages of those quantities do not change. While different climate models disagree on the exact nature of many shifts, a large source of uncertainty is human behavior and policy choices, and many significant changes can be avoided if greenhouse gas emissions fall drastically in the future. Key Points: Coupled Model Intercomparison Project models project changes to the annual cycle of many hydroclimate variables, many of which are more significant than annual mean changesIn the continental United States, there are significant earlier shifts in the annual cycle in a high emissions scenarioSignificant changes to the annual cycle are largely avoided in the lowest‐emissions scenario [ABSTRACT FROM AUTHOR]

The warming climate can considerably affect socioeconomic activities and environmental health conditions in Canada. Climate models play a key role in evaluating the impact of climate change and developing adaption and mitigation strategies corresponding to Canadian regions. This study compares the behavior of climate models participating in the Climate Model Intercomparison Project Phase 6 (CMIP6) with their CMIP5 predecessors in representing a set of climate indices relevant to Canada's agricultural productivity, infrastructure resilience, and environmental health. Our results show that although CMIP5 and CMIP6 multi‐model ensemble mean values for the considered indices are almost similar, the behavior of individual CMIP5 and CMIP6 models or even the model pairs of the same modeling center can be different across Canada. Moreover, the CMIP6 models do not necessarily outperform CMIP5 in comparison to NOAA and NCEP reanalysis datasets in simulating the annual mean and coefficient of variation values for the indices during the historical period. The comparisons between models' simulations also reveal that the envelope of estimated values based on individual CMIP6 models does not cover or overlap with their CMIP5 counterparts or even with other CMIP6 models. Therefore, the CMIP6 models with higher number of simulations do not necessarily provide a larger range of projections over Canadian regions. The divergence between CMIP5 and CMIP6 models' behavior is more obvious under the 8.5 W/m2 forcing in the long‐term horizon. Overall, the estimated sign, magnitude, and spatial pattern of changes in the climate indices depend on the considered climate model and forcing scenario. Plain Language Summary: Climatic conditions play an important role in shaping countries' society, economy, and environment. As Canada gets warmer, the country becomes more vulnerable to changes in temperature and precipitation. Particularly, agricultural production, infrastructure stability, and human and environmental health are at great risks of degradation. Climate models are the most credible tools to project future climate; therefore, their behavior is critical to evaluate the climate change impacts. In this study, the simulations of the state‐of‐the‐art Climate Model Intercomparison Project Phase 6 (CMIP6) and CMIP5 climate models are compared across Canadian regions. In total, 389 climate simulations are used to estimate a set of pertinent climate indices. Results reveal that there is no clear superiority of CMIP6 models over CMIP5 in representing estimated values by NOAA and NCEP reanalysis datasets during the historical period in Canada. Moreover, although the magnitude and spatial pattern of projections by CMIP5 and CMIP6 models are not necessarily similar, both of them show significant changes in temperature and precipitation in the future and considered climate indices, which can be alarming for Canada. Further analyses using bias‐corrected simulations are needed to assess the extent of climate change impacts on Canada's agriculture, infrastructure as well as environmental health. Key Points: Comparisons with NOAA and NCEP reanalysis datasets show that CMIP6 climate models do not necessarily outperform CMIP5 models in CanadaAlthough CMIP6 and CMIP5 multi‐model ensemble mean values are almost similar, the range of projections by individual models is differentThe divergence between CMIP5 and CMIP6 models' behavior is more obvious under the 8.5 W/m2 forcing in the long‐term horizon [ABSTRACT FROM AUTHOR]

The differential climatic impact between the 1.5° versus 2°C warmer worlds is a global concern on the post‐Paris science agenda. The recently released Intergovernmental Panel on Climate Change (IPCC) special report "Global Warming of 1.5ºC" (hereafter SR15) concludes robustly avoided impacts from climate extremes from the 0.5°C less warming globally, based on multiple lines of evidence. However, the methodological uncertainties, including those arising from modeling strategy (i.e., different pathways to achieve the temperature targets), remain a knowledge gap. Here, by comparing simulations with the common Community Atmosphere Model (CAM5) using different modeling strategies (i.e., fully coupled transient, fully coupled quasi‐equilibrium, and atmosphere‐only quasi‐equilibrium simulations), we explored the uncertainty from modeling strategy in the reduced impacts of climate extremes from the half‐degree less warming. While the globally aggregated reduced impacts are reasonably consistent among modeling strategies, substantial quantitative uncertainties of reduced impact exist at regional scales. For temperature extremes, the largest methodological uncertainty exists in northern high‐latitudes, especially in Europe and North America, with spread exceeding 0.8°C. For precipitation extremes, large uncertainties (even opposite signs) are expected scatteredly over the globe, particularly in the populous South Asia for wet extremes, and in South‐East Asia, West North America, and the Amazon for dry extremes, with spread of ∼5%. The different regional responses among modeling strategies result from multiple physical processes, dominated by the transient versus quasi‐equilibrium responses, prescribed sea surface temperatures and aerosol forcing. The comparisons improve the understanding of methodological uncertainties in the projected impacts in light of model setups. Plain Language Summary: To differentiate the climatic impact between the 1.5° versus 2°C warmer worlds, multiple methods and simulations have been employed in previous studies, including in the IPCC special report "Global Warming of 1.5ºC." However, the methodological uncertainties arising from modeling strategies (i.e., different pathways to achieve the 1.5° and 2°C global warming levels), remain a knowledge gap. By comparing simulations using different modeling strategies, we show that while the avoided impact of temperature and precipitation extremes by the 0.5°C less warming is qualitatively consistent among different modeling strategies on a global scale, substantial uncertainties exist at regional scales. Hotspots exhibiting largest methodological uncertainties are seen in northern high‐latitudes for temperature extremes but are scattered over the globe for precipitation extremes. The different regional responses among modeling approaches result from multiple physical processes, dominated by the transient versus quasi‐equilibrium responses, prescribed boundary conditions, and aerosol loading in the model setups. Key Points: Globally aggregated, the avoided impact of climate extremes by 0.5°C less warming is qualitatively consistent among modeling strategiesRegionally, model‐setup dependency is large in northern high latitudes for hot/cold extremes, and scattered globally for wet/dry extremesDifferent regional responses among model setups are dominated by transient versus quasi‐equilibrium responses, prescribed sea surface temperatures, and aerosols [ABSTRACT FROM AUTHOR]

Global warming is leading to increased heat stress in many regions around the world. An extensive number of heat stress indicators (HSIs) has been developed to measure the associated impacts on human health. Here we calculate eight HSIs for global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We compare their future trends as function of global mean temperature, with particular focus on highly populated regions. All analyzed HSIs increase significantly (p < 0.01) in all considered regions. Moreover, the different HSIs reveal a substantial spread ranging from trends close to the rate of global mean temperature up to an amplification of more than a factor of two. Trends change considerably when normalizing the HSIs by accounting for the different scales on which they are defined, but the large spread and strong trends remain. Consistently, exceedances of impact‐relevant thresholds are strongly increasing globally, including in several densely populated regions, but also show substantial spread across the selected HSIs. The indicators with the highest exceedance rates vary for different threshold levels, suggesting that the large indicator spread is associated both to differences in trend magnitude and the definition of threshold levels. These results highlight the importance of choosing indicators and thresholds that are appropriate for the respective impact under consideration. Additionally, further validation of HSIs regarding their capability to quantify heat impacts on human health on regional‐to‐global scales would be of great value for assessing global impacts of future heat stress more reliably. Plain Language Summary: Heat stress caused by high levels of air temperature and humidity is rising globally due to climate change. Various indicators for heat stress have been developed to quantify different facets of how heat impacts people. We use data from climate models to calculate the future evolution of eight heat stress indicators for highly populated regions of the world. The trends of the different indicators vary substantially, with some indicators showing large increases while others only increase modestly. For estimating the severity of heat stress, we calculate how often each indicator exceeds threshold values that indicate different heat stress severity. Many thresholds will be exceeded much more often with rising temperatures. The increases are particularly large for some indicators while others only show small increases. Moreover, the indicators with the strongest trends are often not the ones that show the highest increase in threshold exceedances. For quantifying the impacts of heat stress caused by climate change it is thus important to choose indicators that are appropriate for the respective application. While several indicators were tested on small scales (e.g., in cities or single countries), for global heat stress assessments it is necessary to have more validation studies on regional‐to‐global scales. Key Points: All heat stress indicators increase statistically significantly with global mean temperature but trends reveal a substantial spreadExceedances of impact‐relevant thresholds are strongly increasing globally including in several densely populated regionsFor assessing heat‐related health impacts only indicators and thresholds should be chosen that were validated for the respective application [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]