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Better preparedness for summer heatwaves could mitigate their adverse effects on society. This can potentially be attained through an increased understanding of the relationship between heatwaves and one of their main dynamical drivers, atmospheric blocking. In the 1979-2015 period, we find that there is a significant correlation between summer heatwave magnitudes and the number of days influenced by atmospheric blocking in Northern Europe and Western Russia. Using three large global climate model ensembles, we find similar correlations, indicating that these three models are able to represent the relationship between extreme temperature and atmospheric blocking, despite having biases in their simulation of individual climate variables such as temperature or geopotential height. Our results emphasize the need to use large ensembles of different global climate models as single realizations do not always capture this relationship. The three large ensembles further suggest that the relationship between summer heatwaves and atmospheric blocking will not change in the future. This could be used to statistically model heatwaves with atmospheric blocking as a covariate and aid decision-makers in planning disaster risk reduction and adaptation to climate change.

Bias correction is an essential technique to correct climate model outputs for local or site-specific climate change impact studies. Most commonly used bias correction methods operate on a single variable, which ignores dependency among multiple variables. The misrepresentation of multivariable dependence may result in biased assessment of climate change impacts. To solve this problem, a new multivariate bias correction method referred to as two-stage quantile mapping (TSQM) is proposed by combining a single-variable bias correction method with a distribution-free shuffle approach. Specifically, a quantile mapping method is used to correct the marginal distribution of single variable and then a distribution-free shuffle approach to introduce proper multivariable correlations. The proposed method is compared with the other four state-of-the-art multivariate bias correction methods for correcting monthly precipitation, and maximum and minimum temperatures simulated by global climate models. The results show that the TSQM method is capable of both bias correcting univariate statistics and inducing proper inter-variable rank correlations. Especially, it outperforms all the other four methods in reproducing inter-variable rank correlations and in simulating mean temperature and potential evaporation for wet and dry months of the validation period. Overall, without complex algorithm and iterations, TSQM is fast, simple and easy to implement, and is proved a competitive bias correction technique to be widely applied in climate change impact studies.

Global warming is expected to have far‐reaching impacts on the frequency and intensity of extreme events, but the effects of anthropogenic warming on the emergence seasons of year‐maximum near‐surface wind speed (NSWS) remain poorly understood. We provide a comprehensive map of the changing emergence seasons of year‐maximum NSWS using Coupled Model Intercomparison Project Phase 6 projections. Our analysis reveals a rapid response of synoptic‐scale extreme NSWS to global warming, with consistent spatial patterns observed across various periods and warming scenarios. The most significant increase (∼16%) in the emergence season is projected to occur in December‐January‐February (DJF) over Mid‐high‐latitude Asia by the end of the 21st century. The study also anticipates changes in the emergence seasons of year‐maximum NSWS at a regional scale. These results deepen our understanding of the complex and interconnected nature of global climate change and underscore the need for concerted efforts in addressing this pressing challenge. Plain Language Summary Global warming is indisputably triggering changes in the world's weather systems, leading to more frequent and intense extreme weather events. However, it is unclear how anthropogenic warming affects the timing of the annual strongest near‐surface wind speed (NSWS). In this study, we used state‐of‐the‐art global climate models to create a comprehensive map illustrating these NSWS patterns of response to global warming. We discovered that these changes are consistent across various time periods (near to long term) and warming scenarios (low to high warming), revealing a robust relationship between extreme NSWS and global warming. The most significant change is observed during December‐January‐February in Mid‐high‐latitude Asia, with an increase of about 16% by the end of the 21st century. Our findings suggest that we can expect more year‐maximum NSWS occurs in different regions during specific seasons: December‐January‐February in North America and Asia, March‐April‐May in Africa, June‐July‐August in Asia and West Africa, and September‐October‐November in South America and Australia. These results offer valuable insights for guiding adaptation efforts even if ambitious climate actions manage to limit global warming at a lower level. Key Points Changing emergence seasons of the land year‐maximum near‐surface wind speed (NSWS) map is created There is a rapid response of emergence seasons of year‐maximum NSWS to anthropogenic warming The strongest increase (16%) in emergence season is projected to occur in December‐January‐February over Mid‐high‐latitude Asia

With global climate warming, extreme climate events are becoming more frequent, posing a great threat to crop production. In this study, twelve extreme climate indices (ECIs) were defined to characterize climate events prone to occurring during key phenological stages of wheat. Additionally, eighteen Global Climate Models (GCMs) from the Coupled Model Inter-comparison Project phase 6 (CMIP6) were selected to analyze the spatial–temporal characteristics and trends of these ECIs under four emission scenarios of the future Shared Socioeconomic Pathway (SSP). The Delta Change Method (DCM) was used to correct the bias of GCM data, and the arithmetic mean and Independence Weighted Mean (IWM) were used to aggregate the results of different GCMs to improve the projection accuracy of ECIs. Overall, the IWM ensemble results can better reproduce historical changes of ECIs than multi-model arithmetic mean and any individual GCM. The results indicated that the ECIs across wheat growing area in China were significant spatial heterogeneity during the historical period from 1981 to 2010. Under future climate scenarios, the frequency of extreme high temperature events would significantly increase in most regions, and the intensity will increase by 0.13–0.99 ° and 0.44–2.41 ℃ during 2031–2060 and 2071–2100. However, the stress of extreme low temperature events during wheat growth periods would decrease. Although the changes of extreme precipitation events under different climate scenarios were not significant, these showed considerably spatial differences across wheat growing area. In order to maintain high and stable yield of wheat, it is important to take measures to mitigate the effects of future extreme climate events on wheat production.

We present a long-term assessment of precipitation trends in Southwestern Europe (1850-2018) using data from multiple sources, including observations, gridded datasets and global climate model experiments. Contrary to previous investigations based on shorter records, we demonstrate, using new long-term, quality controlled precipitation series, the lack of statistically significant long-term decreasing trends in precipitation for the region. Rather, significant trends were mostly found for shorter periods, highlighting the prevalence of interdecadal and interannual variability at these time-scales. Global climate model outputs from three CMIP experiments are evaluated for periods concurrent with observations. Both the CMIP3 and CMIP5 ensembles show precipitation decline, with only CMIP6 showing agreement with long term trends in observations. However, for both CMIP3 and CMIP5 large interannual and internal variability among ensemble members makes it difficult to identify a trend that is statistically different from observations. Across both observations and models, our results make it difficult to associate any declining trends in precipitation in Southwestern Europe to anthropogenic forcing at this stage.

The temporal structure of heat waves having substantial human impact varies widely, with many featuring a compound structure of hot days interspersed with cooler breaks. In contrast, many heat wave definitions employed by meteorologists include a continuous threshold‐exceedance duration criterion. This study examines the hazard of these diverse sequences of extreme heat in the present, and their change with global warming. We define compound heat waves to include those periods with additional hot days following short breaks in heat wave duration. We apply these definitions to analyze daily temperature data from observations, NOAA Geophysical Fluid Dynamics Laboratory global climate model simulations of the past and projected climate, and synthetically generated time series. We demonstrate that compound heat waves will constitute a greater proportion of heat wave hazard as the climate warms and suggest an explanation for this phenomenon. This result implies that in order to limit heat‐related mortality and morbidity with global warming, there is a need to consider added vulnerability caused by the compounding of heat waves. Plain Language Summary Heat waves are multiday periods of extremely hot temperatures and among the most deadly natural disasters. Studies show that heat waves will become longer, more numerous, and more intense with global warming. However, these studies do not consider the implications of multiple heat waves occurring in sequence, or “compounding.” In this study, we analyze physics‐based simulations of Earth's climate and temperature observations to provide the first quantifications of hazard from compound heat waves. We demonstrate that compound events will constitute a greater proportion of heat wave risk with global warming. This has important policy implications, suggesting that vulnerability from prior heat waves will be increasingly important to consider in assessing heat wave risk and that heat wave warning systems that currently primarily consider future‐predicted weather should also account for the recent history of weather. Key Points Hazard from temporally compound heat waves will disproportionately increase with global warming Increase is controlled by mean shift in temperature and average local weather, not change in weather Vulnerability from prior heat waves should be considered in assessing heat wave risk

Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other geophysical processes. This paradigm allows for distributed development of individual components within a common framework, unified by a coupler that handles translation between realms via spatial or temporal alignment and flux exchange. Following a similar approach adapted for machine learning‐based emulators, we present SamudrACE: a coupled global climate model emulator which produces centuries‐long simulations at 1‐degree horizontal, 6‐hourly atmospheric, and 5‐daily oceanic resolution, with 145 2D fields spanning 8 atmospheric and 19 oceanic vertical levels, plus sea ice, surface, and top‐of‐atmosphere variables. SamudrACE is highly stable and has low climate biases comparable to those of its components with prescribed boundary forcing, with realistic variability in coupled climate phenomena such as ENSO that is not possible to simulate in uncoupled mode.

The scientific discourse on climate change throughout the US has primarily revolved around changes in mean climate and/or climate extremes. However, little is known about the impacts of climate change on mild weather conditions despite its significant relevance to quality of life. Here, we adopt the concept of “outdoor days” defined as those relatively pleasant days when most people may enjoy outdoor activities (Choi et al., 2024). We project how climate change reshapes seasonality of US outdoor days: relatively large drops in summer, late spring, and early fall; and a significant increase in winter. However, annual outdoor days are projected to change slightly, with notable exceptions. We project relatively large drops in southeast (−23%), south (−19%), and Ohio Valley (−19%), and a significant increase in northwest (14%) toward the end of the century. Our findings have implications for quality of life in different regions, and for nation‐wide travel and tourism. Plain Language Summary Here, we contribute to the understanding of how climate change will influence quality of life in the US by applying the concept of outdoor days—thermal comfort conditions allowing for outdoor activities, such as walking, jogging, and cycling by most people. We project using state‐of‐the‐art global climate models that climate change will shift the seasonality of outdoor days, resulting in less frequent outdoor days in summer and more frequent outdoor days in the other seasons across the country. Our results highlight specific regional hotspots in the US where annual outdoor days could significantly decrease or increase with important implication for quality of life in different climate regions of the US. Key Points We project how climate change reshapes seasonality of US outdoor days Future climate change will likely result in a northwest‐southeast disparity in the projected change of annual outdoor days in the US We provide new evidence of the impact of global warming on the quality of human life, travel, and tourism in the US

Climate change is a major threat to biodiversity as many species are facing the risk of extinction due to their inability to adapt to the changes in temperature, precipitation, and other environmental variables. The impact of climate change on the habitat distribution of Taxus wallichiana, a medicinally important endangered tree species, has not been studied specifically for the Indian Himalayan region (IHR). We assessed the vulnerability of the species to climate change using Ecological Niche Modeling (ENM) in conjunction with two latest global climate models (GCMs) viz., HadGEM3-GC31-LL and IPSL-CM6A-LR, under two future scenarios i.e. Shared Socioeconomic Pathways (SSPs) - SSP126 and SSP585 from Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report, 2023. Based on current distribution of the species and bioclimatic conditions., the Maxent-derived projections indicated significant reduction in its suitable habitat in IHR. Under the moderate scenario i.e. SSP126, suitable habitats are expected to decrease to 6,313,494 ha (10.62% of the total geographical area of IHR) with HadGEM3-GC31-LL and to 4,161,437 ha (7.00%) with IPSL-CM6A-LR from the present distribution area of 8,132,637 ha (13.68%). Under high-emission SSP585 scenario, the predicted habitat area is expected to decline to 4,833,212 ha (8.13%) with HadGEM3-GC31-LL and to 3,204,306 ha (5.39%) with IPSL-CM6A-LR.Annual mean temperature, isothermality, and annual precipitation were important environmental variables impacting the species distribution and models’ predictive capacity. The model outputs clearly predict a gloomy picture under both the future climate scenarios for T. wallichiana emphasizing the need for a targeted conservation effort for the species. .

Rice (Oryza sativa L.) production is an important contributor to China’s food security. Climate change, and its impact on rice production, presents challenges in meeting China’s future rice production requirements. In this study, we conducted a comprehensive analysis of how rice yield responds to climate change under different scenarios and assessed the associated simulation uncertainties of various regional-scale climate models. Simulation was performed based on a regional calibrated crop model (CERES-Rice) and spatially matched climatic (from 17 global climate models), soil, management, and cultivar parameters. Grain-filling periods for early rice were shortened by 2–7 days in three time slices (2030s, 2050s, and 2070s), whereas grain-filling periods for late rice were shortened by 10–19 days in three time slices. Most of the negative effects of climate change were predicted to affect single-crop rice in central China. Average yields of single-crop rice treated with CO2 fertiliser in central China were predicted to be reduced by 10, 11, and 11% during the 2030s, 2050s, and 2070s, respectively, compared to the 2000s, if planting dates remained unchanged. If planting dates were optimised, single-crop rice yields were predicted to increase by 3, 7, and 11% during the 2030s, 2050s, and 2070s, respectively. In response to climate changes, early and single-crop rice should be planted earlier, and late rice planting should be delayed. The predicted net effect would be to prolong the grain-filling period and optimise rice yield.