This study proposes a two-level stacking machine learning approach for predicting rainfall erosivity ( R m ) in Taiwan, providing a flexible alternative to traditional empirical methods. Conventional models rely on limited high-resolution rainfall data and are often region-specific, which limits their accuracy elsewhere. In contrast, the proposed ensemble framework captures complex, non-linear interactions among climatic and topographic variables to improve prediction accuracy. In the first level, six base models were combined, and in the second level, each base model was used as a meta-model to form the ensemble structure. Twenty-eight predictor variables, including climatic and topographic factors, were derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) high-resolution global climate data and a digital elevation model (DEM). To ensure robustness, the modeling procedure was repeated five times using different train–test splits, and final performance metrics were calculated as averages across five datasets. Feature selection using Boruta identified rainfall-related variables as the most important contributors. The ensemble approach significantly improved predictive performance, achieving a root mean square error (RMSE) of 5317 . 92 ± 261 . 23 MJ ⋅ mm ⋅ ha − 1 ⋅ hour − 1 ⋅ year − 1 and a Nash–Sutcliffe efficiency (NSE) of 0 . 67 ± 0 . 02 . The analysis revealed an increasing trend in R m , particularly under higher emission scenarios (SSP3-7.0 and SSP5-8.5), with increases projected in the latter half of the century. These findings highlight the importance of targeted climate mitigation and adaptation strategies for soil conservation and watershed management. This study supports Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land) by improving R m prediction to reduce land degradation and enhance climate resilience. • Two-level stacking ensemble ML framework predicts rainfall erosivity ( R m ) in Taiwan. • Combined six base and meta models with 28 climate and DEM predictors. • Random forest (RF) meta-model achieved best accuracy (NSE = 0.67, RMSE = 5317.92 MJ ⋅ mm ⋅ ha −1 ⋅ hour −1 ⋅ year −1 ). • R m shows increasing trends under high-emission scenarios in late 21st century.

This study offers a comprehensive investigation into the predicting of global solar radiation and the evaluation of climate change effects on insolation, as well as six photovoltaic (PV) technologies—mono-crystalline silicon (m-Si), poly-crystalline silicon (p-Si), amorphous silicon (a-Si), hybrid silicon (h-Si), cadmium telluride (CdTe), and copper indium gallium selenide (CIGS)—across three carbon-intensive economies: South Africa, India, and China, over three timeframes: 2015–2050, 2051–2100, and 2015–2100. Employing Coupled Model Intercomparison Project – Phase 6 (CMIP6) climate datasets within Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), the authors developed 20 empirical, statistical, ensemble, and machine learning models, resulting in the novel SARIMA-CARIMA-GPM hybrid model, which attained exceptional accuracy (R² > 0.94; RMSE < 0.001) in predicting monthly mean global solar radiation. Findings indicate geographical disparities in prospective solar potential: India demonstrates significant improvements in insolation (up to +2.95 %) under SSP585, facilitating long-term solar growth, particularly during the monsoon and autumn seasons. South Africa exhibits annual stability but experiences significant seasonal declines during MAM (−3.0 %), indicating a necessity for focused seasonal photovoltaic deployment. Conversely, China exhibits sustained long-term reductions in irradiance (up to −3.8 %), primarily attributable to enduring pollutants and cloud cover, with winter insolation remaining alarmingly low. To assess performance variations in photovoltaic modules, two innovative analytical instruments—Seasonal Elasticity Analysis Model (SEAM) and Time Horizon Decomposition Model (THDM)—were created, demonstrating that thin-film (a-Si, CdTe, CIGS) and hybrid (h-Si) modules exhibit greater climate resilience compared to traditional crystalline silicon. The results offer practical guidance for enhancing photovoltaic system design, technology choice, and climate-responsive policy measures. • Tri-hybrid SARIMA–CARIMA–GPM boosts GSR prediction (R²>0.94; RMSE<0.001). • Six PV modules compared; thin-film and h-Si show strongest climate resilience. • India gains solar potential (up to +2.95 %); China declines (∼−3.8 %). • South Africa near-neutral annually but MAM losses reach about −3.0 %. • New SEAM and THDM indices reveal seasonal elasticity and time-slice sensitivity.

Extreme rainfall is increasing under climate warming, but its future patterns across Africa remain highly uncertain. Using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, we assess projected changes in annual maximum one-day precipitation (Rx1day) and rare precipitation extremes across African subregions under both SSP2-4.5 and SSP5-8.5 scenarios. Our results show robust intensification of Rx1day by the late twenty-first century, with increases of ~5-10 mm day⁻¹ (15-23 mm day⁻¹) under SSP2-4.5 (SSP5-8.5), largest in convectively dominated equatorial regions such as West, Central, and Northeast Africa. Rare events that historically occurred once every 50 (100) years are projected to recur every ~5-10 ( ~ 6-14) years, with recurrence intervals as short as 2-3 years in equatorial regions under SSP5-8.5. This intensification is driven primarily by thermodynamic moistening associated with radiation-induced warming, while diabatic heating-driven dynamic changes modulate regional responses and account for much of the intermodel spread. A hierarchical emergent-constraint framework based on observed historical global mean surface temperature trends moderates mean Rx1day intensification by ~11-35% without altering its sign. Constrained and unconstrained projections indicate substantial continent-wide increases in population and gross domestic product exposure.

Abstract Cloud feedback remains the main source of uncertainty in climate sensitivity estimated by global climate models (GCMs), largely because subgrid cloud responses are parameterized in GCMs due to their coarse resolution. This study examines cloud feedback in the global 3.25-km Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM 3 km) through a pair of 1-yr atmosphere-only simulations with control and +4-K sea surface temperature perturbations. SCREAM 3 km produces a positive cloud feedback that falls within but at the upper end of the range of Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP phase 6 (CMIP6) models and expert judgment. The positive cloud feedback arises from positive contributions from both high- and low-level clouds, with increases in high-cloud altitude and decreases in low-cloud amount and optical depth playing key roles. The stronger-than-CMIP-average feedback is mainly attributable to the high-cloud altitude feedback, owing to cloud tops rising nearly isothermally in SCREAM 3 km. The positive low-cloud amount feedback is weaker in SCREAM than in GCMs because estimated inversion strength (EIS) increases more dramatically with warming. A coarser 12-km resolution version of SCREAM exhibits a weaker positive cloud feedback than SCREAM 3 km, mainly because its low-cloud-radiative flux is more sensitive to EIS, leading to a stronger negative low-cloud amount feedback. With this process-level assessment of cloud feedback, this study reveals where SCREAM aligns with and diverges from conventional GCMs and expert assessment, providing insights to inform further model improvement and future expert assessment.

The patterns and drivers of Oropouche virus and its primary vector invasions in Asia under global change.

Abstract This study examines future changes in precipitation and temperature across Myanmar using bias-corrected multimodel ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive under shared socioeconomic pathway 2-4.5 (SSP2-4.5) and SSP5-8.5 scenarios. Thirteen general circulation models (GCMs) were evaluated against observed data from 38 meteorological stations (1985–2014), and seven models were selected based on performance metrics. Bias correction was applied using the power transformation method for precipitation and variance scaling for temperature, and its performance was assessed for reproducing both baseline climate and extreme indices. Decomposition of uncertainty sources for the baseline period revealed that model and temporal variability are the dominant contributors to projection uncertainty. Future projections were analyzed for near-future (2021–50), mid-future (2051–80), and far-future (2081–2100) periods. Results indicate increasing precipitation across most regions, particularly during the rainy season, with the central dry zone and coastal areas showing the most notable changes. Minimum temperatures show a consistent warming trend across all scenarios and regions. In contrast, maximum temperatures exhibit mixed trends under SSP2-4.5 but a pronounced increase under SSP5-8.5, with the northern hilly region projected to warm by up to 4.9°C by the end of the century. Extreme indices also show a clear intensification of extremes toward the end of the century, especially under SSP5-8.5. These findings offer essential insights into Myanmar’s future climate risks and provide a scientific foundation for developing region-specific climate adaptation and resilience strategies. Significance Statement This study applies CMIP6 multimodel ensembles to project long-term precipitation and temperature changes across Myanmar under shared socioeconomic pathway 2-4.5 (SSP2-4.5) and SSP5-8.5. By bias-correcting model outputs and selecting high-performing general circulation models (GCMs) and analyzing both mean climate and extreme indices, we provide robust regional and seasonal climate projections through 2100. The decomposition of uncertainty sources for the base period and projection period shows how model and temporal variability affect the projections. The results reveal significant warming and shifting precipitation patterns, particularly affecting the dry and hilly zones. These changes have critical implications for agriculture, water resources, and climate risk management. Our work addresses a regional knowledge gap in climate projection literature and offers actionable information for adaptation planning in one of Southeast Asia’s most climate-vulnerable countries.

• Extreme precipitation patterns across different regions of WNP are evaluated. • Two concentrated precipitation belts are observed throughout the four seasons. • Mid-21st century projections are derived from HighResMIP ensemble means. • Future precipitation tends to get wetter in high intensity region, and vice versa. Extreme precipitation in the western North Pacific (WNP) is jointly controlled by complex circulation systems, which can be better captured by enhancing model resolution. Despite progress in evaluating precipitation in the Coupled Model Intercomparison Project Phase 6 (CMIP6), most assessments have focused on land monsoon regions or individual river basins, leaving the land-ocean transitional WNP insufficiently investigated. This study, leveraging the climate model outputs from the High-Resolution Model Intercomparison Project (HighResMIP) of the CMIP6, examines the spatiotemporal characteristics of precipitation in the WNP and evaluates the simulation biases across different models. The results indicate that most HighResMIP models are capable of simulating spatial patterns of double‑banded precipitation zones that are consistent with observations, with higher skill scores compared to their lower‑resolution counterparts. Among these models, ECMWF-IFS-HR and EC-Earth3P-HR exhibit superior performance in capturing both annual mean precipitation and extreme precipitation characteristics. The multi-model ensemble means from optimal models reduce uncertainty significantly by much narrowed inter-model spread. Precipitation projections in the mid-21st century (2031–2050) exhibit a "wet-get-wetter, dry-get-drier" divergent development characteristic, indicating an eastward expansion and an increased influence of extreme precipitation toward lower- and higher-latitude regions under future warming.

Earth System Models provide spatiotemporally continuous environmental exposure data but remain underused in environmental epidemiology because of uncertainty from measurement errors. We developed a novel latent-variable approach to correct for measurement error characterized by spatiotemporal error covariance, which was derived from comparisons between Coupled Model Intercomparison Project Phase 6 (CMIP6) monthly fine particulate matter (PM2.5) simulations and station-based monitoring data from 5,661 global sites. To demonstrate the utility of the framework, we associated these exposures to birthweight records from 132 Demographic and Health Surveys. The results showed variable correlations between the models and the observations (r=0.40-0.68) as well as widely varying effect estimates across Earth System Models, from a 0.01g (95% confidence interval: -0.85-0.87) reduction to a 15.11g (12.69-17.54) reduction in birthweight per 10μg/m3 increase in PM2.5. After correcting measurement error, the optimal estimate indicated a more precise and consistent reduction of 3.34g (2.57-4.11) in birthweight per 10μg/m3. These findings demonstrate that the negative association between PM2.5 exposure and birthweight is robust to different levels of measurement error embedded in CMIP6-based exposures, and that correction for measurement error in environmental epidemiology can help avoid misestimating the effect by reducing bias and improving consistency.

Abstract Interactions between the tropical Pacific and the tropical Atlantic (TP–TA) play a crucial role in tropical Pacific climate variability and predictability on a broad range of time scales. However, the ability of state-of-the-art climate models to simulate these cross-basin interactions remains uncertain. This study systematically evaluates the representation of TP–TA interactions in 34 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6). While models generally reproduce the spatial patterns of key tropical climate modes, significant biases are found in their amplitudes, seasonality (particularly for the equatorial Atlantic mode), and spectral characteristics. Notably, most models substantially underestimate Atlantic impacts on spatiotemporal aspects of El Niño–Southern Oscillation (ENSO). To disentangle the bidirectional coupling mechanisms, we employ a linear inverse model (LIM) that allows selective isolation of Atlantic-to-Pacific versus Pacific-to-Atlantic coupling. Our analysis reveals two key aspects of TP–TA interactions: 1) internal Atlantic variability enhances Pacific climate variance across interannual and decadal time scales and 2) Pacific-driven Atlantic variability reduces tropical Pacific low-frequency variability. Although these influences qualitatively agree with observations, their simulated intensities are markedly weaker. Furthermore, we identify considerable intermodel spread in representing TA impacts on TP variability, highlighting persistent challenges in achieving robust model consensus. Our findings underscore the need to improve the representation of TP–TA interactions in climate models, particularly through more realistic simulations of tropical Atlantic dynamics and their seasonal evolution, to make progress in seasonal-to-decadal climate predictions.

Abstract. Phase 2 of the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP2) is a registered model intercomparison project (MIP) of the Coupled Model Intercomparison Project phase 7 (CMIP7). The focus of AerChemMIP2 is the quantification of the atmospheric composition, biogeochemical feedbacks, air quality and climate responses to changes in emissions of chemically reactive gases, aerosol particles, and land use. AerChemMIP2 aims to facilitate a better understanding of their relative contributions to changes in atmospheric composition, radiative forcing, and the climate response and feedbacks from the pre-industrial period to the present day and for projected future emission pathways. Some experiments from the first phase of AerChemMIP are requested in the second phase to track changes in the results of CMIP7 compared to phase six of CMIP. New experiments in AerChemMIP2 open scientific opportunities to address knowledge gaps and persistent uncertainties. Specifically, AerChemMIP2 requests experiments (1) to assess the dependence of effective radiative forcing for aerosols on the fidelity of resolved processes and the simulated base climate, (2) to provide first estimates of forcing for hydrogen and individual volatile organic compounds in the context of CMIP, (3) to enable studies on non-linearity in the Earth system response, (4) to understand the response of wild fires to historical forcings, and (5) to quantify the influence of desert dust increases on climate change. AerChemMIP2 further requests variants of the ScenarioMIP-CMIP7 high-end and overshoot scenarios to quantify future responses to policy implementations for air quality management. Diagnostic requests of AerChemMIP2 are made from CMIP7 core experiments to facilitate offline experiments for chemistry and aerosols. The experimental protocol of AerChemMIP2 presented here closely aligns with the CMIP7 core experimental design, and its other registered MIPs. Selected AerChemMIP2 experiments are performed in the Assessment Fast Track (AFT) of CMIP7. Participation of modelling centres in AerChemMIP2 would help to gain new insights for atmospheric composition and implications for air quality in a warming world with rapidly changing emissions.

Anthropogenic climate forcing is altering ocean circulation and water mass distribution across the Southern Ocean, reshaping the habitat of circumpolar marine predators such as threatened crested ( Eudyptes ) penguins. Understanding species vulnerability remains challenging due to substantial uncertainties in climate projections. Here, we integrate two state-of-the-art climate assessment tools—storylines and time of emergence—to evaluate the vulnerability of crested penguins to ocean warming while explicitly addressing projection uncertainties. Using this framework, we select a discrete set of projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that capture qualitatively different global circulation responses and climate sensitivity. Uncertainty in global atmospheric circulation responses, particularly the degree of intensification of the Westerlies, strongly influences both the magnitude and spatial pattern of projected sea surface temperature (SST) warming within penguin foraging habitats. Storylines and climate sensitivity explain a greater proportion of overall projection uncertainty compared to conventional CMIP6 scenario ensembles. We identify two groups of SST sensitivity among crested penguins: (1) highly sensitive species, including Northern Rockhoppers ( E. moseleyi ) and Aotearoa/New Zealand endemic species, and (2) species with broader distributions, such as Southern and Eastern Rockhoppers ( E. chrysocome ) and Macaroni/Royal penguins ( E. chrysolophus/E. schlegeli ), which exhibit spatially heterogeneous exposure and sensitivity. Spatial variability in exposure among widely distributed species highlights opportunities for targeted monitoring to detect early climate change impacts. However, limited data on population dynamics, gene flow, and foraging ecology constrain vulnerability assessments, emphasizing the need for expanded ecological and tracking studies coupled with environmental monitoring. We advocate for interdisciplinary, uncertainty-aware approaches and transparent workflows, including open data and code sharing, to strengthen future climate vulnerability assessments for threatened species.

Abstract. The Land and Land Ice Theme in the Coupled Model Intercomparison Project Phase 7 (CMIP7) represents the current understanding of physical processes in land surface ecosystems, hydrology, cryosphere, and their physical interactions with other Earth system components. Simulations from Earth system models (ESMs) could provide crucial information for assessing planetary safety, such as critical tipping elements, and be used to inform climate risks for improving climate impact assessments and policy decisions. This paper presents a collaborative effort to identify scientific opportunities in the Land and Land Ice Theme of the CMIP7 Data Request. The proposed opportunities build upon advances in ESMs, including new freshwater system and land ice processes being included in CMIP7, as well as the scientific community's demand for high-frequency and sub-grid-scale land surface outputs. In total, 25 variable groups that contain 716 variables have been identified to be potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology. Key reflections from this data request effort include advocacy for closer engagement between the user community and modeling groups, reduction in the technical barriers to tracking existing parameters and defining new variables, and more streamlined variable management. These will be essential to enhance the usability and reliability of CMIP7 outputs for climate and Earth system research and applications to a broad audience that relies on the CMIP7 endeavor.

Abstract. The terrestrial biosphere absorbs about one third of anthropogenic CO2 emissions, thereby significantly slowing human-induced climate change. Its capacity to act as a carbon sink strongly depends on climate conditions, particularly soil moisture (SM), which can constrain plant growth and amplify land–atmosphere feedbacks. Therefore, accurately capturing these effects in Earth System Models (ESMs) is critical. Using dedicated experiments of the Land Feedback Intercomparison Project (LFMIP, an experiment within the Land Surface, Snow, and Soil Moisture Model Intercomparison Project, LS3MIP) from the latest generation of ESMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show that projected SM changes substantially reduce the land carbon sink by the end of the century (2070–2099). This reduction is mainly driven by SM variability, highlighting the importance of SM extremes, which are projected to become more frequent and intense under climate change. Our results confirm those of the previous model generation based on the Global Land–Atmosphere Climate Experiment-Coupled Model Intercomparison Project phase 5 (GLACE-CMIP5). The results show that the strong negative impact of SM changes on the land carbon sink shown for GLACE-CMIP5 is less severe in LFMIP. A more in-depth analysis reveals that this is due at least in part to the specific ESM sampling of the respective experiments, with participating ESMs from CMIP5 generally showing a stronger drying trend. Despite agreement on the negative impact of SM on the land carbon sink in most tropical and mid-latitude ecosystems in both sets of multi-model experiments, there are large intermodel differences in the projected magnitudes. As SM can influence land carbon uptake both directly and indirectly via land–atmosphere coupling, we conduct a contribution analysis on the impact of direct and indirect SM effects on carbon uptake, which reveals that SM–atmosphere interaction dominate SM-induced changes globally. However, models show disagreement on the magnitude of these effects. Intermodel differences arise mainly from varying sensitivities of GPP to SM-related direct and indirect effects, suggesting that differences likely stem from varying representations of water-stress related processes across ESMs. Our findings highlight SM–atmosphere coupling as a critical factor for future land carbon uptake. Improving the representation of water stress processes, plant hydraulics, and vegetation characteristics in ESMs is essential for reducing uncertainty in projections. Maintaining and possibly extending the experimental setup to a larger set of models in future CMIP generations will be key to advancing our understanding of SM-carbon interactions and consequently of the evolution of the land carbon sink under human-induced climate change.

Abstract. This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data unlocking key research avenues in atmospheric science and provides justification for the resources needed to produce this data. Topics within the CMIP7 Atmosphere Theme centre around processes and feedbacks in atmospheric science such as clouds, aerosols and atmospheric chemistry, atmospheric circulation, temperature variability and extremes, radiative forcings, and Earth system model evaluation. These topics are summarised in this paper as scientific “opportunities” which will be realised through CMIP7 experiments and Earth system model outputs. These opportunities were submitted by a thematic group of atmospheric science community representatives combined with an extended consultation process. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making, including supporting the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). As an author group, we also reflect on the process used to collate this data request and make recommendations to future CMIP governance on implementing a consultation on this scale in the future.

Climate models show considerable discrepancies in their future projections around the Atlantic, mainly due to uncertainties in the fate of the Atlantic Meridional Overturning Circulation (AMOC). Climate models suggest a reduction in AMOC strength of 32 ± 37% by 2100 (90% probability, Shared Socioeconomic Pathways 2-4.5 scenario, Coupled Model Intercomparison Project Phase 6). To refine this estimate and reduce its uncertainty, we use four different observational constraint methods. The best one, which provides the lowest leave-one-out error, integrates a large set of observable variables using ridge-regularized linear regression-a method unusual in climate science. It gives an estimate of the AMOC slowdown of 51 ± 8% (90% probability), i.e., a weakening ∼ 60% stronger than suggested by the multimodel mean. This refinement mainly results from correcting a bias in South Atlantic surface salinity, consistent with recent studies emphasizing its role in the proximity to an AMOC tipping point. This more substantial AMOC weakening has key implications for future adaptation strategies.

Abstract Tropical South American summer precipitation is primarily controlled by the intensity and position of the South American monsoon and the intertropical convergence zone, both of which respond to sea surface temperature anomalies over the surrounding tropical oceans. Our analysis examines how well contemporary, high-complexity Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6) simulate the summer precipitation distribution and its interannual variability under preindustrial climate conditions. Specifically, we investigate how El Niño–Southern Oscillation (ENSO) and Atlantic Niño—two major zonal modes of variability in the tropical ocean—shape tropical South American precipitation through remote atmospheric teleconnections. The quality of the simulated climatological mean state and interannual variability across models is primarily evaluated using pattern correlations with the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) product. The three models with the highest and the three models with the lowest field correlation with the ERA5 reference are selected for a more detailed study of their representation of major modes of variability and associated teleconnection patterns. We show that models with large discrepancies in the location and abundance of core monsoon precipitation also typically fail to accurately represent atmospheric deep convection and teleconnections associated with the zonal modes. Differences in the ability to simulate South American summer precipitation, even under preindustrial forcings, emphasize the importance of selecting an appropriate model for studying the regional hydroclimate. Our study further calls for future research using high-resolution models that explicitly resolve deep convection to realistically capture South American monsoon rainfall. Significance Statement Tropical South America receives abundant rainfall from December through February, which is dominated by the South American monsoon system and the deep convection in the intertropical convergence zone. Naturally occurring climate modes in the surrounding tropical oceans, such as El Niño–Southern Oscillation and Atlantic Niño, also drive large variability in summer rainfall. Accurately representing how rainfall over tropical South America responds to modes of climate variability in preindustrial simulations is essential for evaluating the robustness and realism of climate models. Our study shows that models with more realistic atmospheric convection (upward vertical motion in the lower to midtroposphere) better depict the observed rainfall patterns and year-to-year changes over tropical South America, including the rainfall patterns shaped by the modes of variability. Models that better replicate these influences can be instrumental not only in understanding the future of South American rainfall but also in attribution studies of extreme events like droughts and floods.

Abstract. This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data pertaining to Earth systems science, and provides justification for the resources needed to produce this data. Topics within the CMIP7 Earth System (CMIP7-ES) theme centre around tracking of flows of energy, carbon, water and other fluxes across domains, and constraining feedbacks between these cycles and the climate system. These topics are summarized in this paper as scientific “opportunities” describing specific model intercomparison experiments and use cases for next-generation Earth System Model (ESM) output. These opportunities were submitted by modelling groups and scientific consortia following an extended public consultation process. Contained within each opportunity are requests for groups of Climate &amp;amp; Forecasting (CF) variables, which are bundled into variable groups representing all data required to address the opportunities' needs. Novel opportunities in CMIP7 compared with previous phases will include running `emissions-driven' simulations that integrate carbon emissions and removal scenarios with updated representations of the global carbon cycle, expanded variable groups needed to model marine trophic interactions and biogeochemistry, and data needed to understand the risk of global tipping points, among others. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and support the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). We argue that CMIP7-ES data will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making. As an author group we also reflect on the evolution of the CMIP7-ES data request as a part of a deliberative process in support of the global CMIP program.

Abstract This study evaluates the ability of the Coupled Model Intercomparison Project phase 6 (CMIP6) climate models to simulate the observed effects of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) on Indian summer monsoon rainfall (ISMR) variability. Using observational data and the large ensemble historical simulations of seven CMIP6 models from 1950 to 2014, we applied a cyclostationary linear inverse model (CS-LIM) to isolate the impacts of tropical Pacific SSTAs, Indian Ocean SSTAs, and their interaction on the interannual variability of ISMR. Overall, these CMIP6 models well reproduced the observed enhanced (reduced) ISMR variability from Pacific SSTAs (Indian Ocean SSTAs and the Indo-Pacific interaction), though with varying spatial patterns and magnitudes. Among them, CESM2 and Energy Exascale Earth System Model version 2.0 (E3SM-2-0) showed the best agreement with observations for the effects of Pacific SSTAs and the Indo-Pacific interaction, respectively. Composite analysis of ISMR anomalies during the developing phases of pure and co-occurring El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) events revealed that the impacts from Pacific SSTAs were captured reasonably well by E3SM-2-0, CESM2, MIROC6, and MPI-ESM1-2-LR, while E3SM-2-0 also showed the best agreement with observations for the effects from the Indo-Pacific interaction. However, all seven models exhibited substantial biases in simulating the Indian Ocean SSTA impacts on ISMR, particularly during pure El Niño events. Overall, this study provides new insights into how individual CMIP6 models simulate the isolated impacts from the tropical Pacific and Indian Oceans, which have important applications for improving ISMR predictions and interpreting ISMR future projections.

Abstract Rapid changes and increasing climatic variability across the widely varied Köppen-Geiger regions of northern Europe generate significant needs for adaptation. Regional planning needs high-resolution projected temperatures. This work presents an integrative statistical bias correction framework that incorporates Vision Transformer (ViT), Convolutional Long Short-Term Memory (ConvLSTM), and Geospatial Spatiotemporal Transformer with Attention and Imbalance-Aware Network (GeoStaNet) models. The framework is evaluated with a multicriteria decision system, Deep Learning-TOPSIS (DL-TOPSIS), for ten strategically chosen meteorological stations encompassing the temperate oceanic (Cfb), subpolar oceanic (Cfc), warm-summer continental (Dfb), and subarctic (Dfc) climate regions. Norwegian Earth System Model (NorESM2-LM) Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs were bias-corrected during the 1951–2014 period and subsequently validated against independent historical observations (1951–2014) of day-to-day temperature metrics, extreme value distributions (99th percentile), and thermodynamic coupling (Diurnal Temperature Range). The ViT showed improved performance (Root Mean Squared Error (RMSE): 1.01 °C; R 2 : 0.92), allowing for production of credible bias-corrected projections. Under the SSP5-8.5 scenario, the Dfc and Dfb climate zones are projected to warm by 4. 8 °C and 3. 9 °C (Summer T m a x ), respectively, by 2100, with expansion in the diurnal temperature range by more than 1. 5 °C. The Time of Emergence signal first appears in subarctic winter seasons (Dfc: ~ 2032), signifying an urgent need for adaptation measures. The presented framework offers station-based, high-resolution estimates of uncertainties and extremes, with direct uses for adaptation policy over high-latitude regions with fast environmental change.

ABSTRACT Compound wind‐precipitation extremes (CWPE) are among the most impactful compound climate extremes under climate change. Using three CWPE definitions (strict co‐occurrence, CWPE; temporal offset, CWPE_day_offset; and spatial offset, CWPE_space_offset), we systematically analyse the spatiotemporal evolution of CWPE over China during 1980–2022. Based on multi‐model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we further evaluate historical simulations, project future changes under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, and quantify the sources of projection uncertainty. CWPE shows a pronounced “more in the east, less in the west” pattern, with hotspots in eastern Sichuan and coastal South China. Nationally, CWPE decreases during 1980–2010 but shifts to an increasing tendency during 2011–2022. Introducing temporal and spatial offsets markedly increases annual‐mean CWPE frequency and expands the affected area, with a stronger enhancement from spatial offsets. CWPE is strongly seasonal, occurring mainly in spring (MAM) and summer (JJA) and least in winter (DJF). CMIP6 models reproduce the large‐scale spatial pattern but generally overestimate CWPE magnitude, and biases are generally larger when offsets are considered. Projections indicate persistent increases in CWPE frequency under all scenarios, strongest under SSP5‐8.5, with central and eastern China as the main hotspots and larger increases toward later decades and higher emissions. Uncertainty decomposition indicates that internal variability dominates projection uncertainty in the early period, but its relative contribution gradually weakens over time. In the mid‐to‐late period, model uncertainty becomes the dominant source and continues to increase, while scenario uncertainty remains the smallest contributor. This systematic assessment of CWPE helps inform climate‐risk management in China under climate change. Our results show that CWPE definitions strongly affect trend estimates and spatial identification; introducing spatiotemporal offsets increases the number of identified events and their spatial coverage, thereby providing a clearer signal of potential risk changes for climate‐impact assessment.