Climate CH2025 documents and explains past, present, and future climate change in Switzerland using the latest climate model data, providing the scientific basis for updating the National Adaptation Strategy after 2025. The Climate CH2025 scenarios use climate models from the Coupled Model Intercomparison Project (CMIP), integrating CMIP5-era Regional Climate Models (hereafter called RCMs) and CMIP6 General Circulation Models (GCMs) through both established and newly developed approaches based on Global Warming Levels (GWLs). Observations show that climate response in Switzerland has been particularly pronounced in comparison to other global land regions with mean near-surface air temperatures in 2024 exceeding the preindustrial reference period by 2.9 °C. This is a warming rate about two times faster than on global average. Most models simulate a substantially lower warming trend over this period. The recent warming was likely substantially enhanced by internal variability and by a decline of atmospheric aerosol loads since the 1980s. Regardless, a mismatch identified between RCMs and GCMs, where western Europe and Switzerland warm consistently more in GCMs than RCMs, in particular in spring and summer, limits confidence in the RCMs. This warming mismatch presents the main methodological challenge for Climate CH2025.Several methodological choices were made in Climate CH2025 to reduce the influence of the RCM-GCM warming mismatch on Swiss climate change projections. The first was to set the “present day” base period to 1991-2020, consistent with the current norm period of the World Meteorological Organization. The observed global warming from the preindustrial period to the present day was used to calculate when each GCM reaches a given GWL, defined as a 30-year mean relative to preindustrial conditions. CMIP6 GCMs were brought in to incorporate the latest regional warming estimates, which were used in a regional time adjustment step that ensured RCMs and GCMs warmed the same amount regionally at each GWL. Once regional warming was aligned, local climate responses at 1.5 °C, 2 °C, and 3 °C of global warming could be reported. This method we call the “Block-Time-Shift" (BTS) approach. An advantage of using GWLs is that they relate warming on the global scale to Swiss warming, without relying on specific details in socioeconomic emissions scenarios. A disadvantage is that BTS cannot provide fully transient timeseries. Here we show how the BTS approach shaped results in Climate CH2025, particularly in comparison to earlier Swiss climate scenarios. We report on user feedback on GWLs from communication and technical standpoints and provide guidance for updating workflows from change at fixed time points to change at fixed points in global temperature.

The European continent has experienced severe wildfire activity in multiple regions as a result of extreme drought and heat events in recent years, particularly in 2003, 2017 and 2018. Quantifying how climate change has altered the likelihood of extreme wildfire occurrence and its accompanying fire weather conditions remains challenging due to strong internal climate variability and short observational records. Here, we quantify changes in the probability of extreme wildfire conditions considering four fire weather indicators: the Canadian Fire Weather Index (FWI), drought conditions (i.e. 3-month Standardized Precipitation Evapotranspiration Index; SPEI-3M), heat (maximum temperature; Tmax) and atmospheric moisture demand (vapor pressure deficit; VPD). First, we assess the return periods of the four fire weather indicators during these extreme wildfire periods under observed climate using CERRA reanalysis data (2001-2020). Second, we quantify how the likelihood of conditions that describe observed extreme wildfire periods changes between preindustrial, present, 2°C and 3°C global warming levels, by bootstrapping data from the 100-member Community Earth System Model Large Ensemble (CESM2-LE). We show that the probability of fire weather conditions during observed extreme wildfire periods increases nonlinearly with global warming. The probability of the FWI as found during these extreme wildfire periods doubled from preindustrial to present levels and is projected to increase three- and seven-fold under 2°C and 3°C of global warming, respectively. For SPEI-3M, VPD and Tmax we find even stronger increases. Our results highlight the substantial benefits of limiting global warming to well below 2°C for reducing wildfire-relevant climate extremes.

The vertical velocity in convective clouds (wc) mediates convective anvil development and global moisture transport, influencing Earth's energy budget, but has yet to be estimated globally over long periods due to the absence of spaceborne retrievals. Here, a method for estimating wc given vertical profiles of in-cloud temperature, pressure, and latent heating rate is presented and assessed. The method relies on analytical models for the approximately linear relationship between wc and condensation rate (q(center dot)yc) in convective clouds, which we derive from steady-state and non-steady-state plume models. We include in our analysis a version of q(center dot)yc/wc derived from the supersaturation rate in convective clouds, recently presented in the 2024 paper by Kukulies et al. We assess the accuracy of wc estimates against convective cloud simulations run with different model cores and spatial resolutions in both tropical and midlatitude environments. The velocity estimates exhibit lower uncertainties and higher precision in the tropics than they do in the midlatitudes. Vertical velocity is estimated to within '1 m s21 for most samples in the tropics. Potential applications, validation against future satellite mission retrievals, and approaches for improving the estimation are discussed. SIGNIFICANCE STATEMENT: A method for estimating the vertical velocity (wc) in clouds using readily available satellite data products is proposed and evaluated, providing a way to quantify for the first time wc over broad temporal and spatial scales. The results of this study demonstrate that the method's vertical velocity estimates are within a few meters per second of the true values with relatively high precision. By estimating wc with available satellite data, longer-term historical records of convective vertical velocity and large-scale dynamics can be generated.

A refactored atmospheric dynamical core of the ICON model implemented in GT4Py, a Python-based domain-specific language designed for performance portability across heterogeneous CPU-GPU architectures, is presented. Integrated within the existing Fortran infrastructure, the new GT4Py dynamical core is shown to exceed ICON OpenACC performance. A multi-tiered testing strategy has been implemented to ensure numerical correctness and scientific reliability of the model code. Validation has been performed through global aquaplanet and prescribed sea-surface temperature simulations to demonstrate model's capability to simulate mesoscale and its interaction with the larger-scale at km-scale grid spacing. This work establishes a foundation for architecture-agnostic ICON global climate and weather model, and highlights poor strong scaling as a potential bottleneck in scaling toward exascale performance.

Mesoscale convective systems (MCSs) are a critical global water cycle component and drive extreme precipitation events in tropical and midlatitude regions. However, simulating deep convection remains challenging for modern numerical weather and climate models due to the complex interactions of processes from microscales to synoptic scales. Recent models with kilometer‐scale horizontal grid spacings offer notable improvements in simulating deep convection compared to coarser‐resolution models. Still, deficiencies in representing key physical processes, such as entrainment, lead to systematic biases. Additionally, evaluating model outputs using process‐oriented observational data remain difficult. This study presents an ensemble of MCS simulations with spanning the deep convective gray zone ( from 12 km to 125 m) in the Southern Great Plains of the U.S. and the Amazon Basin. Comparing these simulations with Atmospheric Radiation Measurement (ARM) wind profiler observations, we find greater sensitivity in the Amazon Basin compared to the Great Plains. Convective drafts converge structurally at sub‐kilometer scales, but some deficiencies remain. In both regions, simulated up and downdrafts are too deep and extreme downdrafts are not strong enough. Furthermore, Amazonian updrafts are too strong. Overall, we observe higher sensitivity in the tropics, including an artificial buildup in vertical kinetic energy at scales of , suggesting a need for 250 m in this region. Nevertheless, bulk convergence—agreement of storm‐average statistics—is achievable with kilometer‐scale simulations within a 10% error margin with 1 km providing a good balance between accuracy and computational cost.

This paper evaluates seasonal forecasts of weather types (WTs), i.e., recurring large-scale atmospheric patterns, which have been developed using a clustering method using large-scale predictors to represent precipitation variability across the United States. Forecast quality is assessed using two seasonal hindcast products: from the operational weather community, hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as hindcasts from the Community Earth System Model, version 2 (CESM2), a community resource developed by the National Science Foundation (NSF) National Center for Atmospheric Research, a climate research center. WTs are described in terms of their associated precipitation anomalies and examined in light of their relationship with established climate teleconnections. The spatial precipitation patterns associated with each WT are less well captured in the forecasting systems than the large-scale variables from which the WTs are derived. The WT patterns themselves are well represented in both seasonal forecasting systems, though, on average, ECMWF is slightly closer to observations. Forecasted WT frequency results show that both prediction systems have similar skill, with most differences depending on season and WT. Winter WT frequencies are generally more predictable than summer. Both forecast systems capture well the frequency rank order but underestimate the interannual frequency spread, which could be partially due to ensemble averaging. Analysis shows that forecasting of climate teleconnection indices alone would not be sufficient to represent the precipitation variability associated with the WTs. Comparable results from two initialized Earth system prediction models that originate from different sides of the weather-climate and operations-research spectrum are encouraging and contribute to WT forecasting and multimodel initialized prediction efforts. SIGNIFICANCE STATEMENT: The purpose of this study is to evaluate how well seasonal forecasts capture largescale weather patterns that are associated with precipitation variability across the United States. Our results show that large-scale patterns are better captured than precipitation patterns. Given the societal importance of precipitation, forecasting based on associated weather types could complement existing seasonal prediction systems.

Organized deep convection plays a critical role in the global water cycle and drives extreme precipitation events in tropical and mid-latitude regions. However, simulating deep convection remains challenging for modern weather forecasts and climate models due to the complex interactions of processes from microscales to mesoscales. Recent models with kilometer-scale (km-scale) horizontal grid spacings (∆x) offer notable improvements in simulating deep convection compared to coarser-resolution models. Still, deficiencies in representing key physical processes, such as entrainment, lead to systematic biases. Additionally, evaluating model outputs using process-oriented observational data remains difficult. In this study, we present an ensemble of MCS simulations with ∆x spanning the deep convective grey zone (∆x from 12 km to 125 m) in the Southern Great Plains of the U.S. and the Amazon Basin. Comparing these simulations with Atmospheric Radiation Measurement (ARM) wind profiler observations, we find greater ∆x sensitivity in the Amazon Basin compared to the Great Plains. Convective drafts converge structurally at sub-kilometer scales, but some discrepancies, such as too-deep up- and down-drafts and too-weak peak downdrafts in both regions or too-strong updrafts in Amazo- nian storms remain. Overall, we observe higher ∆x sensitivity in the tropics, including an artificial buildup in vertical velocities at five times the ∆x, suggesting a need for ∆x≤250 m. Nevertheless, bulk convergence – agreement of storm average statistics – is achievable with km-scale simulations within a ±10 % error margin, with ∆x=1 km providing a good balance between accuracy and computational cost.

While high-resolution future climate data are increasingly available for the contiguous United States, there has been limited focus on Alaska and Hawaii. Our study provides high resolution daily climate data -precipitation and temperature -at 10 km for Alaska and 1 km for Hawaii, based on 23 climate models from the Coupled Model Intercomparison Project Phase 6 and four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). The dataset includes outputs from different bias correction techniques (seven for precipitation, six for temperature). While one method (i.e., empirical quantile mapping) generally outperforms the others, its performance can vary by climate quantity, region, or climate model, motivating our aim to provide an ensemble dataset of bias-corrected outputs. Our results show that, on average, Alaska and Hawaii are projected to become warmer (6.7 °C and 5.8 °C) and wetter (46% and 31%) by the end of the 21st century for high-emissions scenarios compared to the historical period, despite large regional variability. Our dataset aims to support climate research, mitigation and adaptation strategies, and risk assessments for Alaska and Hawaii.

Mesoscale convective systems (MCSs) can lead to severe disasters in Northeast China but are insufficiently studied in this region with complex terrain and complicated interactions between the mesoscale and synoptic‐scale processes. This study compares the MCSs from satellite products, state‐of‐the‐art global reanalysis (ERA5), and kilometer‐scale simulations in Northeast China, based on cloud top brightness temperature and precipitation data. Results show that ERA5 severely underestimates the number of MCSs due to inadequacies in characterizing moist deep convection. Despite kilometer‐scale simulations indicating much larger MCS areas compared to satellite products, through further comparison with hourly gauge observations, it is found that the kilometer‐scale simulation employing nudging can outperform satellite products in terms of MCS‐associated metrics including mean precipitation, precipitation intensity distribution, and diurnal cycle of precipitation. This study shows that kilometer‐scale simulations are promising tools for studying MCSs, rivaling satellite products.

Abstract Examining large‐scale projected changes in streamflow and flood extent (e.g., inundation) for Alaska is essential for raising awareness of flood hazards under a changing climate and supporting broad‐scale adaptation planning. Therefore, we examine projected changes in peak streamflow timing and magnitude using a physically based hydrologic model. For model inputs, we utilize climate simulations conducted at 4‐km horizontal grid spacing over Alaska from 2005 to 2016, providing a historical and future pseudo‐global warming scenario. Analysis of hydrographs reveals the peak timing shifts slightly earlier in the year for most of Alaska's streams. The change in peak magnitude is more heterogeneous across the state, with the northernmost region showing the highest projected increases. The changes in timing are driven by temperature, while precipitation and temperature drive the changes in magnitude. These changes are then transformed into inundation maps, showing a similar albeit more muted pattern compared to the changes in magnitude.

The Caribbean and Central American hydroclimate is understudied and complex in part due to its data sparsity, varied topographies, and multi-faceted interactions with the tropics and mid-latitudes. Recent work developed a refined and comprehensive understanding of the observed hydroclimate that has yet to be explored in global circulation models. This study investigates the simulation of the Caribbean hydroclimate using a suite of station and gridded observational datasets, the Community Earth System Model version 1 (CESM1) at high (0.25 × 0.25°) and low (0.9 × 1.25°) resolution, CESM2 at low (0.9 × 1.25°) resolution, the Coupled Model Intercomparison Project phase 6 (CMIP6) High-Resolution Model Intercomparison Project (HighResMIP), and the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and Earth System Research (GFDL-SPEAR). The simulated climatologies (1983–2014) of the annual rainfall cycle and total moisture fluxes, and climatological regressions of sea surface temperatures (SST), sea-level pressure (SLP), and zonal/meridional low-level winds onto indices of seasonal Caribbean rainfall totals are calculated to investigate inter-model differences and their comparison to observations. Generally, fully coupled CESM, GFDL-SPEAR-MED, and CMIP6 simulations underestimate precipitation across the Caribbean, with some improvements using high-resolution (< 0.5°) simulations. The underestimations are largest during the Early-Rainy Season (ERS; mid-April to mid-June). Coupled models also show a moisture divergence bias associated with a stronger/west-displaced North Atlantic Subtropical High (NASH), a weaker / southward displaced Intertropical Convergence Zone (ITCZ), and stronger Caribbean Low-Level Jet (CLLJ). Precipitation and large-scale dynamic biases in experiments with observation-based SSTs are smaller, regardless of their spatial resolution, suggesting SST biases in coupled models may contribute to precipitation and circulation biases. The findings emphasize the importance of both high-resolution and accurate simulation of coupled dynamical interactions in global circulation models to accurately simulate the Caribbean’s hydroclimate, and, therefore, provide reliable future climate projections for the region.

Global kilometer‐scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model‐observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi‐model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter‐model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process‐oriented diagnostics to guide future model development.

The DYAMOND project (Stevens et al. 2019) provides an intercomparison framework for state-of-the-art global convection-permitting models with km-scale horizontal grid spacing that can directly simulate convective storms. We recently assessed the fidelity of the convective storms simulated by DYAMOND models using a novel feature tracking technique (Feng et al. 2023) and found a surprisingly large inter-model spread in the simulated frequency of ordinary deep convection and mesoscale convective systems (MCSs), as well as their associated precipitation. Recent works also showed that different feature tracking algorithms have significant impacts on estimating MCS characteristics including frequency, size, lifetime and precipitation (Prein et al. 2023). To further investigate how feature tracking methods affect the evaluation of global MCS simulations and our understanding of convective organization in observations and DYAMOND simulations, we are organizing a new international initiative called MCSMIP (MCS tracking Method Intercomparison Project). Preliminary results from several different feature trackers show that DYAMOND models generally underestimate observed MCS precipitation amount and their contribution to total precipitation in the tropics (Fig. 1), and the simulated MCS precipitation is too intense. However, some models have notable differences in MCS frequency and characteristics among the trackers. Potential paths towards more process-oriented model diagnostics to better understand the differences in simulated MCS and precipitation characteristics will be discussed. Figure 1. (a) Observed MCS contribution to total precipitation during DYAMOND Phase II, (b) model relative mean difference (%) from observations in the tropics. Each group of bars in (b) is from a feature tracker: PyFLEXTRKR, MOAAP, TOOCAN, tobac, TAMS, and simpleTrack, and each bar denotes a DYAMOND model. References Feng, Z. et al. (2023). Mesoscale Convective Systems in DYAMOND Global Convection-Permitting Simulations. Geophys. Res. Lett., doi: 10.1029/2022GL102603. Prein, A. et al. (2023). Km-Scale Simulations of Mesoscale Convective Systems (MCSs) Over South America – A Feature Tracker Intercomparison. DOI: 10.22541/essoar.169841723.36785590/v1.

In recent years, numerous flood events have caused loss of life, widespread disruption, and damage across the globe. These devastating impacts highlight the importance of a better understanding of flood generating processes, their impacts, and their variability under climate and landscape changes. Here, we argue that the ability to better model flooding is underpinned by the grand challenge of understanding flood generation mechanisms and potential impacts. To address this challenge, the World Meteorological Organization-Global Energy and Water Exchanges (GEWEX) Hydrometeorology Panel (GHP) aims to establish a Global Flood Crosscutting project to propagate flood modeling and research knowledge across regions and to synthesize results at the global scale. This paper outlines a framework for understanding the dynamics and impacts of runoff generation processes and a rationale for the role of a Global Flood Crosscutting project to address these challenges. Within this Global Flood Crosscutting project, we will establish a common terminology and methods to enable the global research community to exchange knowledge and experiences, and to design experiments toward developing actionable recommendations for more effective flood management practices and policies for improved resilience. This harmonization of rich perspectives across disciplines will foster the co-production of knowledge primed to advance flood research, particularly in the current period of heightened climate variability and rapid change. It will create a new transdisciplinary paradigm for flood science, wherein different dimensions of mechanistic understanding and processes are rigorously considered alongside socioeconomic impacts, early warning communications, and longer-term adaptation to alleviate flood risks in society.

Puerto Rico is a tropical island that frequently receives heavy rainfall from a variety of systems, including tropical cyclones like Hurricane Maria (2017), mesoscale convective systems (MCSs), and isolated convection. Its two distinct rainy seasons are dictated by moisture convergence associated with the North Atlantic Subtropical High, while sea breezes and complex topography influence precipitation on the mesoscale. Previous research has examined how tropical precipitation could change in a future climate, showing a decrease in precipitation by 2100 using global climate models (GCMs). However, relatively little research has been conducted using convection-permitting climate models over the tropical Atlantic to understand how precipitation extremes could change in a warmer climate. Here, we fill this gap by dynamically downscaling a 0.25 degree GCM 10-member ensemble to 3 km using the Model Prediction Across Scales (MPAS) model for extreme precipitation events in a current (2001-2021) and future climate (2041-2061) over Puerto Rico. We show that MPAS is largely able to reproduce extreme precipitation events in the current climate when compared to observations and captures a variety of systems. We explore how future changes in extreme rainfall events in the early rainy season, which are largely driven by MCSs and isolated convection, compare to changes in the late rainy season, which are primarily due to tropical cyclones.

Hydroclimate volatility refers to sudden, large and/or frequent transitions between very dry and very wet conditions. In this Review, we examine how hydroclimate volatility is anticipated to evolve with anthropogenic warming. Using a metric of ‘hydroclimate whiplash’ based on the Standardized Precipitation Evapotranspiration Index, global-averaged subseasonal (3-month) and interannual (12-month) whiplash have increased by 31–66

This paper presents the first-ever continental-scale convection-permitting simulations over South America for three water years of different ENSO phases, corresponding to an ENSO neutral year (2018/19), an EI Nino year (2015/16), and a La Nina year (2010/11), using the Weather Research and Forecasting (WRF) model at 4-km grid spacing. The model performance has been validated against precipitation derived from satellite, surface observations, and surface air temperature from reanalysis. The evaluation shows a promising skill at reproducing the observed multi-scale spatiotemporal characteristics of precipitation and temperature, such as the seasonal and sub-seasonal variability, the diverse patterns of diurnal cycle, and deep convective clouds. Sensitivity simulations quantify the impacts of cumulus parameterization, grid spacing, and spectral nudging. Results indicate that a tested scale-aware convection scheme has little benefit, and the model performance degrades as horizontal resolution decreases. Spectral nudging can reduce the precipitation bias over some tropical and subtropical regions but exacerbates the wet bias over the Andean Mountains. A noteworthy model deficiency shared in all simulations is the excess orographic precipitation, a problem in association with the overly active afternoonevening convection possibly resultant from under-representation of clouds and missing cloud-aerosol interaction, though the uncertainty of observational data might contribute to the wet bias as well. These results provide useful guidance for improving the model physics. The overall encouraging agreement between the 4-km model simulations and observations provides confidence in the usage of the established model configuration for regional climate downscaling and climate change projections over South America.

Low wind chill temperatures can have negative impacts on human health and the capability of performing outdoor activities. An open question is how climate change is projected to impact this hazard in high latitude land regions. Here we focus on changes in the magnitude and timing of extreme wind chill days (i.e., days with wind chill temperatures below -34.4 degrees C) in response to future changes in large-scale mean-state climate conditions in Alaska. We find a future reduction in extreme wind chill days, especially in northern Alaska and at lower elevations where most of the population resides. Moreover, the extreme wind chill days' mean date shifts by up to two weeks later in the future, with a narrower seasonal distribution compared to the historical period. These changes are primarily attributed to increased temperatures rather than changes in wind speed. Our finding highlights how this hazard decreases under future large-scale mean-state climate conditions, with likely positive impacts for human health and an increased capability to perform outdoor activities.