Abstract. Future hydrological projections exhibit significant discrepancies among models, undermining confidence in the predicted magnitude and timing of hydrological extremes. Here we show that observation-constrained changes in global mean terrestrial water storage (TWS), excluding Greenland and Antarctica, could be approximately 83 mm lower than raw projections from the Inter-Sectoral Impact Model Intercomparison Project phase 3b (ISIMIP3b) by the end of this century under both the low (SSP1-2.6) and high (SSP3-7.0) future forcing scenarios. Notably, the 95th percentile upper bounds are substantially reduced from 2 to −96 mm under the low-emissions scenario and from 8 to −105 mm under the high-emissions scenario, revealing a notable overestimation of global freshwater availability in the raw model projections. Global models are intricate process representations, making it challenging to isolate causes of their differences with observations. However, by leveraging the emergent constraint (EC) methodology and inter-model spread to empirically adjust biases against observations, we derive more tightly constrained estimates of future TWS changes than those obtained from conventional, unconstrained approaches. The EC-corrected estimates are substantially lower than the raw ISIMIP3b projections, implying that current water resource planning may underestimate the severity of future water shortages, particularly if global water demand remains stable or continues to rise. Our findings pinpoint the urgent need to reduce model uncertainties and enhance the reliability of future hydrological projections to better inform water resource management and climate adaptation strategies.

Abstract. Climate extremes exact a heavy toll on society, with adverse impacts unequally distributed across populations. In this perspective, we outline key challenges and opportunities for advancing research on understanding societal impacts of climate extremes. We identify three key challenges: limited availability and quality of impact data, difficulties in understanding the processes leading to impacts and lack of reliable impact projections. We argue that there is a window of opportunity to address several dimensions of these challenges, and we highlight recent examples and ongoing developments that hold transformative potential for the research field. We conclude with a call to build momentum by fostering interdisciplinary research and collaboration across sectors.

Abstract. Ever-worsening climate change increases near-surface air temperatures for almost the entire Earth and threatens living organisms and human society. While annual mean changes are frequently used to quantify past and expected future changes, the increase is rarely uniform throughout the year. In addition, the shape of the annual cycle and its changes can differ considerably between regions around the globe. Therefore, we perform a global analysis resolving the annual cycle and its changes in different regions, focusing on diagnostics that can be evaluated for the variety of existing annual cycle shapes (e.g., single and double waves, different timing of seasons, etc.). Many previous studies relied on parameter-based methods, assuming a sinusoidal shape of the mean annual cycle. We introduce the Functional Data Analysis (FDA) approach, representing the mean annual cycle by a linear combination of Fourier bases. The FDA methodology does not require any prior assumptions about the shape of the temperature seasonal cycle except periodicity and allows to quantitatively assess various aspects of the seasonal cycle shape. The evolution of the mean annual cycle is estimated from daily long-term mean temperature values, which are converted to functional form. We concentrate on diagnostics that evaluate the absolute change in temperature, its seasonal slope, the position of the maximum, and the amplitude of the annual cycle. We analyze two reanalysis datasets (coupled CERA20C and atmospheric ERA5) and a subset of five CMIP6 Earth system models (ESMs). Observed changes in the second half of the 20th century are assessed, and the ability of ESMs to represent them is evaluated. Further, the changes projected for the end of the 21st century under the SSP3-7.0 pathway are analyzed. Among other results, we highlight distinct differences between the two reanalyses, especially over equatorial and polar regions across diagnostics. Our approach also reveals that differences in the historical period between 1951–1980 and 1981–2010 can be negative during (short) parts of the year in many regions. Further, the ESMs future projections show different rates of warming between seasons, resulting in changes in the amplitude. The largest amplitude increase is projected over the Mediterranean region, and the largest decrease over the Arctic Ocean, the latter being due to the considerably stronger warming in the Northern Hemisphere winter. The ESMs also project a delayed maximum near the poles and an earlier maximum in many tropical continental regions. In Europe, the southern and eastern regions experienced a delay of the maximum of up to 10 d, whereas a slightly earlier maximum is found for northern Europe. A similar dipole pattern can be seen between eastern and western regions in North America. Regarding the slope of the annual cycle, higher latitudes detect a higher magnitude of change in the historical period than lower latitudes. The geographical pattern remains the same for future slope changes, with the magnitude twice as high in most regions. The FDA diagnostics introduced here can be tailored for different purposes and applied to different climatic variables, with no need to make any prior assumptions about the annual cycle shape. Potential applications include, e.g., explicitly evaluating the climate model performance or ensemble mean and spread assessment beyond annual or seasonal means.

Abstract. Resilience is a property of social, ecological, social-ecological and biophysical systems. It describes the capacity of a system to cope with, adapt to and innovate in response to a changing surrounding. Given the current climate change crisis, ensuring conditions for a sustainable future for the habitability on the planet is fundamentally dependent on Earth System (ES) resilience. It is thus particularly relevant to establish a model that captures and frames resilience of the ES, most particularly in physical terms that can be influenced by human policy1. In this work we propose that resilience can serve as a theoretical foundation when unpacking and describing metastable states of equilibrium and energy dissipation in any dynamic description of the variables that characterise the ES. Since the impact of the human activities can be suitably gauged by the planetary boundaries (PBs) and the planet's temperature is the net result of the multiple PB variables, such as CO2 concentration and radiative forcing, atmospheric aerosol loading, atmospheric ozone depletion, etc, then resilience features arise once conditions to avoid an ES runaway to a state where the average temperature is much higher than the current one. Our model shows that this runaway can be prevented by the presence of metastable states and dynamic friction built out of the interaction among the PB variables once suitable conditions are satisfied. In this work these conditions are specified. As humanity moves away from Holocene conditions, we argue that resilience features arising from metastable states might be crucial for the ES to follow sustainable trajectories in the Anthropocene that prevent it run into a much hotter potential equilibrium state.

Abstract. Full-scale Earth System Models (ESMs) are too computationally expensive to keep pace with the growing demand for climate projections across a large range of emissions pathways. Climate emulators, reduced-order models that reproduce the output of full-scale models, are poised to fill this niche. However, the large number of emulation techniques available and lack of a comprehensive theoretical basis to understand their relative strengths and weaknesses compromise fundamental methodological comparisons. Here, we present a theoretical framework that connects disparate emulation techniques and use it to understand potential sources of emulator error focusing on memory effects, hidden variables, system noise, and nonlinearities. This framework includes popular emulation techniques such as pattern scaling and response functions, relating them to less commonly used methods, such as Dynamic Mode Decomposition and the Fluctuation Dissipation Theorem (FDT). To support our theoretical contributions, we provide practical implementation guidance for each technique. Using pedagogical examples including idealized box models and a modified Lorenz 63 model, we illustrate the expected errors from each emulation technique considered. We find that response function-based emulators outperform other techniques, particularly pattern scaling, across all scenarios tested. Potential benefits and trade-offs from incorporating statistical mechanics in climate emulation through the use of the FDT are discussed, along with the importance of designing future scenarios for ESMs with emulation in mind. We argue that large-ensemble experiments utilizing the FDT could benefit climate modeling and impacts communities. We conclude by discussing optimal use cases for each emulator, along with implications for ESMs based on our pedagogical model results.

Abstract. This study explores changes in agricultural drought event characteristics in projections of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for different future scenarios based on three Shared Socioeconomic Pathways (SSP). To quantify the intensity of agricultural droughts, the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6) with a 65-year reference period is applied to the simulations of 18 ESMs. In a first step, these ESMs are evaluated based on performance metrics and pattern correlations of drought related variables including precipitation and approximated reference evapotranspiration with reanalysis datasets including ERA5 and CRU. With this we extend the model benchmarking performed in the third chapter of the IPCC AR6 by 15 years and additional variables. In a second step we analyze global and regional projected SPEI6 distributions to estimate and characterize the changes in agricultural drought in the future based on multi-model means of change rates, distributions and relative area covered by specific events. We quantify the change of drought index values for 42 IPCC AR6 WG1 reference regions individually with a focus on those with most harvest area and find negative trends in water budget and SPEI for higher emission scenarios in most of them, particularly in the Mediterranean and other arid regions. This agrees with other recent studies. Increasing reference evapotranspiration emerges as the dominant driver for drier conditions in these regions. What is considered as the driest 2.3 % months during 1950–2014 is projected to be the new normal or moderate condition in arid regions by 2100, following a high emission future scenario (SSP5-8.5). For this scenario, 40 % of the harvest regions surface is considered to be under extreme drought conditions during Northern Hemisphere autumn. Under a low emission scenario (SSP1-2.6) with an expected global warming of 1.8 °C it would be less than 10 %. Our results show a significant difference between future scenarios regarding distribution shifts and spatial extent of extreme drought conditions in harvesting regions and can serves as a foundation for further impact and mitigation studies.

Abstract. The Greenland ice sheet is melting at an accelerating rate due to the warming climate. In order to understand the potentially important ice-climate feedback processes, evolving ice sheets need to be included in global climate models. Here, we present results from the first bi-directional coupling of the Earth System model NorESM2 with the ice sheet model CISM2 for the Greenland ice sheet under an extended high emission SSP5-8.5 forcing from 1850 to 2300. In our simulation, the ice-mass loss between 1850 and 2300 is equivalent to 1.4 m of sea-level rise. Comparing simulation results to an otherwise identical simulation with a fixed Greenland ice sheet, we see the same global trends in air, ocean and sea-ice changes. The main signals are a 10 °C global air temperature increase from 2000 to 2300, a reduced maximum AMOC at 26.5° N from average 23 to 9 Sv and an all-year free Arctic by 2200. Similar to other coupled CMIP models, the warming trend dominates the changes of the climate components. At the regional scale, elevation changes become an important part of the Greenland surface mass balance, accounting for 20 % of the SMB change by 2200 and for 49 % in 2300. By the year 2300, the ablation area covers 93 % of the ice area. With a low climate sensitivity and relatively weak polar amplification in NorESM2, these results are on the lower end of the spectrum of expected ice mass-loss under CMIP6 model forcing.

Abstract. Simulating the Earth's climate is an important and complex problem, thus climate models are similarly complex, comprised of millions of lines of code. In order to appropriately utilize the latest computational and software infrastructure advancements in Earth system models running on modern hybrid computing architectures to improve their performance, precision, accuracy, or all three; it is important to ensure that model simulations are repeatable and robust. This introduces the need for establishing statistical or non-bit-for-bit reproducibility, since bit-for-bit reproducibility may not always be achievable. Here, we propose a short-simulation ensemble-based test for an atmosphere model to evaluate the null hypothesis that modified model results are statistically equivalent to that of the original model. We implement this test in version 2 of the US Department of Energy's Energy Exascale Earth System Model (E3SM). The test evaluates a standard set of output variables across the two simulation ensembles and uses a false discovery rate correction to account for multiple testing. The false positive rates of the test are examined using re-sampling techniques on large simulation ensembles and are found to be lower than the currently implemented bootstrapping-based testing approach in E3SM. We also evaluate the statistical power of the test using perturbed simulation ensemble suites, each with a progressively larger magnitude of change to a tuning parameter. The new test is generally found to exhibit more statistical power than the current approach, being able to detect smaller changes in parameter values with higher confidence.

Abstract. We assess the evolution of Northeast Atlantic and German Bight storm activity using both model simulations and observational data. Our analysis includes the CMIP6 multi-model ensemble and the Max Planck Institute Grand Ensemble (MPI-GE) under CMIP6 forcing, evaluated across historical forcing and three future emission scenarios. Storm activity is quantified via upper percentiles of geostrophic wind speeds, derived from horizontal gradients of mean sea-level pressure. Observational datasets are employed to benchmark and validate the modeled storm characteristics, enhancing the robustness of our assessment. We detect robust downward trends for Northeast Atlantic storm activity in all scenarios, and weaker but still downward trends for German Bight storm activity. In both the multi-model ensemble and the MPI-GE, we find a projected increase in the frequency of westerly winds over the Northeast Atlantic and northwestesrly winds over the German Bight, and a decrease in the frequency of easterly and southerly winds over the respective regions. We also show that despite the projected increase in the frequency of wind directions associated with increased cyclonic activity, the 95th percentiles of wind speeds from these directions decrease, leading to lower overall storm activity. Lastly, we detect that the change in wind speeds strongly depends on the region and percentile considered, and that the most extreme storms (>99th percentile) may become stronger or more likely in the German Bight in a future climate despite reduced overall storm activity.

Abstract. Effective disaster risk management requires approaches that account for multiple interacting hazards, dynamic vulnerabilities, and institutional complexity. Yet many existing risk assessment methods struggle to reflect how these risks evolve in practice. This paper explores multi-hazard risk dynamics through stakeholder interviews across five European regions (Veneto, Scandinavia, the North Sea, the Danube Region, and the Canary Islands). Stakeholders described how exposure and vulnerability shift over time due to climate change, urban development, and socio-economic dependencies. The interviews highlight governance challenges and the critical role of institutional coordination, as well as synergies and asynergies in DRR measures, where efforts to reduce one risk can unintentionally increase another. By foregrounding real-world experiences across diverse hazard landscapes and sectors, this study offers empirical insights into how multi-hazard risk is perceived and managed. It underscores the need for flexible, context-sensitive strategies that bridge scientific assessment with decision-making on the ground.