Orals: Wed, 6 May, 08:30–10:15 | Room 0.31/32
The Indo-Gangetic Plains (IGP) are one of India’s most densely populated and agriculturally vital regions, making them highly sensitive to changes in monsoon rainfall. Projections under high-emission scenarios indicate that seasonal mean rainfall may decline across much of the IGP, while very heavy and extreme short-duration rainfall events are expected to become more frequent and intense. High-resolution regional climate simulations under the CORDEX-CORE framework provide a detailed assessment of these changes. Multiple ensemble members of RegCM4, driven by MPI-ESM-MR, MIROC5, and NorESM, were evaluated against observations, showing that the model reproduces the spatial distribution and intensity of rainfall patterns over the IGP reasonably well, though it tends to slightly overestimate wet-day frequency. Analysis of climate indices indicates that seasonal mean rainfall and wet-day counts are projected to decline across much of the region by the late 21st century, while very heavy and extremely heavy rainfall events are expected to increase, particularly over the northern belt and Himalayan foothills. The upper tail of daily rainfall is projected to rise by 1.9–4.9%, reflecting an intensification of extreme events under warming conditions. Spatial patterns suggest a reduction in moderate rainfall events over the lowlands, with the northern IGP increasingly prone to intense rainfall episodes. To explore the mechanisms behind these extremes, different dynamical configurations of RegCM4—including hydrostatic, non-hydrostatic, and convection-permitting modes—were employed. Composite analyses of wind, temperature, geopotential height, horizontal moisture flux convergence, and moist static energy indicate that enhanced low-level convergence, intensified monsoon trough dynamics, stronger temperature gradients, increased atmospheric moisture, and higher convective available potential energy drive extreme rainfall events. Lead-lag diagnostics show that these conditions develop several days in advance, highlighting the combined influence of large-scale circulation, orographic forcing, and localized convection. Multi-model simulations, including REMO2015, COSMO, and their ensemble mean, confirm that these thermodynamical and dynamical patterns are robust across models. The study emphasizes the significance of high-resolution, multi-model, and multi-mode regional climate simulations in capturing both broad monsoon dynamics and localized extremes, offering crucial insights for adaptation, disaster management, and water and agricultural planning in one of the world’s most climate-sensitive and socioeconomically vital regions.
How to cite: Bhatla, R.: Observation, Simulation and Projection Approach to Rainfall Extremes over Indo-Gangetic Plains, INDIA, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10849, https://doi.org/10.5194/egusphere-egu26-10849, 2026.
In the present study, the new version of the regional climate model REMO, i.e., REMO2020, will be applied over the South Asia domain as part of the Deutsche Forschungsgemeinschaft (DFG)-funded project titled “Modeling South Asian climate in relation to irrigation and land use changes using a modified scheme for soil hydrology and an interactive vegetation scheme in a regional climate model (MOSAIC)”. The new version has been updated in several aspects compared to the earlier version, REMO2015. Key updates include the implementation of a default lake module (FLake), a three-layered snow module, and a state-of-the-art MACv2-SP aerosol climatology. The model has already been implemented over the European domain and outperforms the previous version in simulating regional climate, especially extremes.
The MOSAIC project will utilize REMO2020 to simulate the South Asian region at a 12 km (0.11°) resolution, emphasizing the monsoon dynamics. Boundary conditions will be provided by ERA5 data, which includes 49 vertical levels, covering the period from 1985 to 2015. To enhance the assessment, REMO2020 outputs will be compared with the existing CORDEX-CORE REMO2015 for precipitation and temperature variables. Additional analyses will also be conducted to assess the model’s effectiveness in representing monsoonal processes.
How to cite: Rai, P., Pietikäinen, J.-P., Pollinger, F., and Paeth, H.: Implementation of a new version of the regional climate model REMO over South Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10280, https://doi.org/10.5194/egusphere-egu26-10280, 2026.
The dynamical downscaling of global climate scenarios with coupled regional Earth system models is one of the most important tools to deliver accurate and applicable predictions for communities. However, uncertainties related to model setups are often systematic and exhibit spatiotemporal structures. Calibrating such systems and quantifying systematic model uncertainties thus remain a central challenge, as bias corrections typically violate fundamental physical principles, such as mass and energy conservation, and may lead to unrealistic local climate sensitivity.
Here, we apply a range of objective calibration strategies to identify optimal model setups for the individual components of the Baltic Region Earth System Model, IOW-ESM. In particular, model sensitivities to changes within a set of parameters are identified using perturbed parameter ensembles and various types of surrogate models. It is thus possible to optimize the parameter set regarding a chosen set of metrics.
Comparing a range of surrogate models and optimization techniques, we find that all optimization strategies have limits in their applicability. In particular, the danger of overfitting the parameter sensitivities is large, and global optimization algorithms in high-dimensional spaces tend to find non-optimal local error minima. Keeping these limits in mind, we were able to significantly reduce model biases of the downward-directed shortwave radiation at the surface and improve the 2-meter temperature in the Baltic Sea region.
How to cite: Voelker, G. S., Börgel, F., Gröger, M., Karsten, S., Naumov, L., and Meier, M. H. E.: Perks and perils of objectively calibrating regional Earth system models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14522, https://doi.org/10.5194/egusphere-egu26-14522, 2026.
Regional climate simulations over China are strongly influenced by land–atmosphere interactions. Yet many widely used regional modeling systems still rely on land-surface schemes that oversimplify subgrid heterogeneity and require homogeneous grid setups between land and atmosphere. To address these limitations, we develop a new regional coupled system, CWRF-CoLM, which interactively links the Climate-Weather Research and Forecasting model (CWRF; a climate extension of WRF) with the Common Land Model (CoLM).
Built on the CPL7 flux coupler, the coupling design has two key features. First, it preserves CoLM’s native data structures so that subgrid land processes are represented explicitly rather than collapsed into an “effective” surface grid. Second, it enables cross-resolution configurations in which the atmospheric and land components run on different horizontal grids, while maintaining consistent exchanges of state variables and surface fluxes via coupler-based mapping.
We perform a multi-decadal regional climate integration over China for 1990-2022 and evaluate CWRF-CoLM against a baseline configuration using the original CWRF land-surface setup. The long-term simulations show that CWRF-CoLM substantially improves climatological performance, including reduced systematic biases in near-surface climate and more realistic surface heat fluxes and their partitioning (Bowen ratio). Overall, the CPL7-based coupling strategy, together with explicit retention of land subgrid structure, enhances the fidelity of long-term regional climate simulations and provides a robust platform for land-atmosphere process studies and regional climate downscaling.
How to cite: Zhang, H., Wu, B., Dai, Y., and Liang, X.-Z.: Development and multi-decadal evaluation of the CWRF–CoLM regional land–atmosphere coupled system over China , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4737, https://doi.org/10.5194/egusphere-egu26-4737, 2026.
Due to climate change, extreme precipitation events have become increasingly common over Europe, resulting in flash floods such as in Germany in 2021 or Spain in 2024. Regional climate models (RCMs) are key tools to project the mitigation and adaptation challenges regarding these events in the 21st century. However, to provide accurate results, it is necessary to find the optimal modelling setup for the research domain and validate the simulations against reference datasets of past measurements.
In this study, the validation of several regional climate model simulations using the new RegCM5 RCM with a horizontal resolution of 10 km has been carried out. The model was set up with different parametrization scheme combinations for each run to assess which combination most accurately reproduces the monthly temperature and precipitation fields of the reference datasets. To analyse the model performance in reproducing extreme precipitation, the monthly frequency of days with precipitation over 10 mm (RR10) was also validated.
The simulation period is short, i.e. 2010–2011, with a spin-up year of 2009. However, the period suits the validation purposes as it involves both an extremely wet and dry year with 2010 holding the record for the highest, and 2011 the lowest sum of yearly precipitation in Hungary. The initial and boundary conditions for temperature, geopotential, specific humidity, horizontal wind components and sea surface temperature were obtained from the ECMWF ERA5 reanalysis dataset. HuClim served as the reference for Hungary, which is a quality-controlled, gridded and homogenized database on a horizontal resolution of 0.1°, whereas for the rest of the domain the measurement-based, gridded E-OBS dataset with the same horizontal resolution was used.
The early results for the Hungarian part of the domain show that the simulated temperature fields are acceptable for almost all the combinations. However, there are substantial differences between the different set ups in the monthly sum and extreme precipitation, for example, the Tiedtke convection scheme provides better results for 2010, whereas the Kain-Fritsch convection scheme is more accurate in 2011.
How to cite: Divinszki, F. T., Kis, A., and Pongrácz, R.: Implementation and validation of the RegCM5 regional climate model on a Central European domain for extreme precipitation conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-648, https://doi.org/10.5194/egusphere-egu26-648, 2026.
The accurate simulation of Mesoscale Convective Systems (MCSs) is a critical benchmark for the skill of regional climate models (RCMs), as these systems are principal drivers of high-impact weather and hazardous flash floods across Europe.
However, evaluating MCSs in climate ensembles has been historically hindered by a lack of tracking-compatible variables and the difficulty of distinguishing organized, self-sustaining convection from Europes frequent frontal precipitation.
To address this, we introduce the EMMA-Tracker (Evolution-based MCS Model Assessment), a novel algorithm designed to identify and track MCSs using only standard model output variables..
This design choice ensures physical consistency when comparing observations with RCM ensembles. The tracker utilizes a series of physics-based post-processing filters, to isolate genuine MCSs based on their full spatiotemporal lifecycle.
We first present a new 27-year reference warm season climatology (1998–2024) generated by applying the EMMA-Tracker to IMERG precipitation and ERA5-derived instability.
This dataset reveals that MCS contribution to heavy hourly precipitation (P99.9) exceeds 60% across most of continental Europe and 80% over parts of the Mediterranean.
Building on this benchmark, we investigate the representation of MCSs within the different generations of the EURO-CORDEX ensembles.
This process-oriented evaluation provides a pathway to understand how mesoscale organization evolves in RCMs and offers insights into the uncertainties of future projections of high-impact convective weather in a warming climate.
How to cite: Kneidinger, D. and Maraun, D.: Mesoscale Convective Systems over Europe: A Comparison of CMIP5 and CMIP6-driven EURO-CORDEX Ensembles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11108, https://doi.org/10.5194/egusphere-egu26-11108, 2026.
The rapid expansion of the CMIP ensemble provides a broader sampling of Earth system behavior, but it also increases the reliance of regional downscaling and applied modeling on transparent and defensible model sub-selection. This challenge is particularly relevant for CORDEX CORE-2, where modeling centers must downscale only a limited number of Earth System Models (ESMs) despite substantial inter-model differences in historical fidelity, structural redundancy and projections uncertainty. Here we present a structured, multi-criteria framework to guide CMIP6 ESM selection for CORDEX CORE-2 downscaling while maintaining cross-domain consistency through a single shared subset of driving models. We evaluate 45 CMIP6 ESMs across nine CORDEX domains using ERA5 as the reference for 1981–2014, applying an extensive set of common and region-specific diagnostics spanning temperature, precipitation, circulation, humidity, sea level pressure, and sea surface temperature. To reduce redundancy, metric contributions are weighted by uniqueness estimated from inter-metric dependence. Model independence is quantified using cosine similarity of standardized performance vectors to identify highly similar models. Model behavior beyond the historical period is characterized using SSP3–7.0 simulations (2015–2100) for the subset of models with the required outputs, assessing regional temperature and precipitation responses to preserve diversity in both thermodynamic and hydrological behavior. We further test alternative aggregation strategies and evaluate ranking robustness using Monte Carlo convergence experiments, showing that stable rankings typically require a large fraction of the full metric suite. Overall, the framework produces reproducible model rankings and provides a practical pathway for selecting ensembles that balance historical credibility, structural diversity, and representative regional responses, thereby improving the interpretability and utility of CORDEX CORE-2 downscaling experiments.
How to cite: Ashfaq, M.: CORDEX-CORE2 Model Selection Framework for Regional Downscaling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8492, https://doi.org/10.5194/egusphere-egu26-8492, 2026.
Deep convective storms play a central role in the global circulation and hydrological cycle and are responsible for a large fraction of extreme precipitation in many regions. Yet they have long been among the most persistent challenges for numerical weather and climate models. The transition from hydrostatic to kilometer-scale (km-scale) convection-permitting regional climate models (RCMs) has fundamentally changed how these storms are represented, enabling the explicit simulation of convective dynamics rather than relying solely on parameterization schemes. However, key physical processes such as turbulent entrainment, microphysical interactions, and surface–atmosphere coupling remain only partially resolved, raising important questions about model fidelity and robustness.
In this contribution, I assess how well kilometer-scale regional climate models simulate deep convective storms across a range of grid spacings and modeling configurations. Using idealized and real-case CP-RCM simulations, I analyze grid-spacing sensitivities in bulk storm properties such as precipitation intensity, vertical mass flux, and storm lifetime, as well as structural convergence in storm morphology and updraft dynamics. The results demonstrate that while bulk precipitation statistics can appear well constrained at kilometer scales, they often arise from compensating errors between microphysics, turbulence, and dynamics, masking deficiencies in the underlying storm processes.
Finally, I show examples in which land–atmosphere coupling and surface heterogeneity exert a first-order control on convective initiation and organization, highlighting the importance of consistent coupling in regional climate simulations. The findings have direct implications for the interpretation of CORDEX and other km-scale RCM ensembles, and point to priority areas for next-generation regional climate model development aimed at more physically grounded projections of extreme rainfall.
How to cite: Prein, A. F., Wang, D., Kukulies, J., and Zhang, Z.: How Well Do We Simulate Deep Convective Storms in Kilometer-Scale Regional Climate Models?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6185, https://doi.org/10.5194/egusphere-egu26-6185, 2026.
Posters on site: Wed, 6 May, 16:15–18:00 | Hall X5
Climate model simulations often differ from observational data, resulting in different projections of future temperature characteristics for a given geographical region. While the magnitude of relative climate changes tends to be consistent across different models, the absolute temperature characteristics can show substantial variation when evaluated against observations. Global Climate Models (GCMs) provide valuable insights into climate change on a global scale at predefined Warming Levels (WLs). A WL is defined as a specific higher temperature threshold relative to a designated reference period or observational baseline. One of the main sources of uncertainty in WL assessment is the timing of when the threshold is reached in relation to the reference period (e.g. 1976–2005). Moving from a global to a regional scale requires downscaling from the coarser resolution typically found in GCMs (approximately 100-150 km) to the finer resolution found in RCMs (approximately 10 km). Consequently, the timing of reaching a specific WL can be accurately assessed at regional and local scales by using high-resolution RCM simulations and the corresponding high-resolution observational data for the region of interest. The REtuning Climate Model Outputs (RECMO) method is introduced as a strategy to mitigate discrepancies among various RCM simulations. This method targets the reduction of uncertainties arising from the divergent climatic baselines described by different models across various WLs. The reference for the WLs is based on observations, and not on the raw model outputs. Our study focuses on the expected changes under different WLs, namely: 1.5 °C, 2 °C and 3 °C. The RECMO methodology is applied here to seven high-resolution raw and bias-corrected EURO-CORDEX and Med-CORDEX outputs for the Carpathian Region. The major towns and cities of the region (including four capitals) are involved in the research as follows: Budapest and Debrecen (Hungary), Bratislava and Kosice (Slovakia), Uzhhorod (Ukraine), Bucharest and Cluj (Romania), Beograd (Serbia). Present research consists of climate indices (e.g. tropical night, summer day, consecutive dry days) based on the following daily meteorological variables: precipitation, minimum and maximum temperature, mean temperature. Our results show that the timing of certain expected changes in these climate indices can differ by up to a decade, depending on whether the computation is based on raw or bias-corrected data. It is clear that the temporal aspect is a crucial factor in preparing for expected changes and developing adaptation strategies. Our findings also highlight the importance of bias-corrected RCM data and reliable high-resolution observational data in the field of climate science.
How to cite: Torma, C. Z., Simon, C., and Kis, A.: REtuning Climate Model Outputs (RECMO method) at regional and local level in the Carpathian Region, Phase II: climate indices, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-298, https://doi.org/10.5194/egusphere-egu26-298, 2026.
The prediction of extreme weather can be quite challenging due to its rare occurrence and a lack of observations, yet it is generally projected to become more frequent and impactful as our climate warms into the future. In regions like the southeast United States (US), where extreme weather is a frequent occurrence, generating accurate projections of extreme weather outbreaks is becoming more relevant. Regional climate models are useful tools for generating such projections, especially when used at resolutions capable of directly simulating convective processes. Location and model configuration can impact a model’s ability to represent severe weather and climate properly, and the apparent model performance can differ depending on the assessed quantity (i.e. precipitation, temperature, wind, etc.). This study aims to understand which configurations of the International Centre for Theoretical Physics regional climate model RegCM version 5 (RegCM5) can best represent convective environments in the US, specifically applying the model to severe weather outbreaks.
We utilize RegCM5 at a convection-resolving resolution of 3 km to test over 100 different configurations with changes to the model’s vertical resolution, cloud microphysics and planetary boundary layer parameterizations, soil moisture initialization, and soil moisture time step. This presentation assesses four quantities: convective available potential energy, convective inhibition, updraft helicity, and precipitation, and compares the output to Storm Prediction Center tornado track data, NASA’s GPM IMERG precipitation dataset, and ERA5 Reanalysis data.
How to cite: Porter, R. and O'Brien, T.: Using Case Studies to Test and Tune a Regional Climate Model for Severe Weather Applications in the United States, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-301, https://doi.org/10.5194/egusphere-egu26-301, 2026.
A clear sign of climate change is the increased frequency of certain weather and climate extremes, which trend can be observed in the South-Eastern European region, including the Carpathian Basin. Heatwaves are among those weather related phenomena that pose serious health risks, being not only dangerous to the human body but also to natural ecosystems and various sectors of the economy. These periods are expected to occur more frequently, with greater intensity and longer duration in the future, in addition, heatwave days may appear earlier in the year and become more frequent after the end of summer.
In this study a heatwave detection method is introduced based on daily mean temperature thresholds optimalized for Hungary. Three main characteristics are counted: duration, intensity and the highest daily mean temperature during the heatwave originally performed on daily measurement data series of a given station. This method was implemented for fine scale regional climate model (RCM) simulations through using grid cell averages for the location of interest. For this research 5 RCMs of the EURO-CORDEX initiative were chosen (CCLM, HIRHAM, RACMO, RCA, REMO) available at a horizontal resolution of 0.11° following the RCP8.5 scenario. Beside the raw simulations, different bias-corrected versions of the above-mentioned RCMs were analysed, namely: the RCM projections available from the EURO-CORDEX initiative using MESAN as reference data, the FORESEE-HUN database and BC-HUCLIM which is a set of bias-corrected simulations created specifically for this research. For the latter, the internationally widely used percentile-based quantile mapping method was applied for bias correction using the quality controlled HuClim data as reference. The process was carried out on a monthly scale for each raw simulation separately.
The results are presented for two cities: Budapest, the capital of Hungary, and Szeged, the third populous city in Hungary, located in the south-eastern part of the country. The average heatwave characteristics are calculated for the reference period 1976-2005, and the changes are expressed for the averages of two future time slices (2021-2050 and 2070-2099). In addition, the occurrence of heatwave days are analysed throughout the year according to the simulations from the different databases. Our results indicate that the average intensity of heatwaves may increase to a greater extent in the south-eastern area of Hungary, and the duration may increase by 3-9 days on average by the end of the 21st century. Under the RCP8.5 scenario, heatwave days are projected to occur between late April and early October at least once out of 30 years during the period 2070-2099.
How to cite: Simon, C., Kis, A., and Torma, C. Z.: Projected changes of heatwave occurrences and characteristics in Hungary using raw and bias-corrected EURO-CORDEX simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-348, https://doi.org/10.5194/egusphere-egu26-348, 2026.
Extreme precipitation poses an increasing risk through flooding, infrastructure damage and loss of life, with further intensification expected under ongoing global warming. Reliable quantification of current and future extreme precipitation requires large ensembles of climate simulations at sufficiently high spatial and temporal resolution.
Here, we present EURO-SUPREME (EURO-SUb-daily PRecipitation extrEMEs), a new large-ensemble dataset of sub-daily precipitation extremes derived from the EURO-CORDEX EUR-11 (0.11°) simulations. The dataset is based on regional climate model (RCM) downscalings of CMIP5 global climate models and provides annual maximum precipitation accumulations for durations ranging from 1 to 72 hours. It combines evaluation simulations with a 35-member ensemble of historical and future projections under the RCP8.5 scenario, resulting in nearly 5,000 simulation years.
We evaluate the dataset across multiple European regions and disentangle the relative contributions of the driving global models and RCMs to biases in extreme precipitation characteristics. In addition, we demonstrate the use of EURO-SUPREME as a benchmark for convection-permitting climate simulations, illustrated by a case study over Belgium. Finally, we analyse projected changes in the intensity and frequency of extreme precipitation events as a function of global warming level.
EURO-SUPREME provides a consistent and statistically robust basis for model evaluation, intercomparison and climate-change impact and risk assessments of sub-daily precipitation extremes.
References
- Dierickx, A., Dewettinck, W., Van Schaeybroeck, B., De Cruz, L., Caluwaerts, S., Termonia, P., and Van de Vyver, H.: EURO-SUPREME: sub-daily precipitation extremes in the EURO-CORDEX ensemble, Earth Syst. Sci. Data, 17, 6747–6762, https://doi.org/10.5194/essd-17-6747-2025, 2025.
- Van de Vyver, H., Van Schaeybroeck, B., and De Cruz, L.: Subdaily Precipitation Extremes in the EURO-CORDEX 0.11° Ensemble (Version 2), World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/EUCOR_prec_v2, 2025.
How to cite: Dierickx, A., Dewettinck, W., Van Schaeybroeck, B., De Cruz, L., Caluwaerts, S., Termonia, P., and Van de Vyver, H.: EURO-SUPREME: A large-ensemble dataset of sub-daily precipitation extremes from EURO-CORDEX, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1954, https://doi.org/10.5194/egusphere-egu26-1954, 2026.
Weather and climate in mountainous regions are strongly affected by topography, which shapes temperature, precipitation, and wind systems on local scales. The topography can trigger and exacerbate extreme events such as downslope windstorms and heavy convective precipitation. These regions are also particularly sensitive to climate change - higher elevations generally experience faster warming. Nevertheless, our understanding of local-scale atmospheric phenomena in complex terrain remains limited, partially because reliable observations are sparse, and most climate simulations are too coarse to resolve the relevant processes. Recent advances in computational power and various downscaling techniques have partly alleviated this problem, giving rise to multiple multi-model ensembles of km-scale climate simulations (<4 km grid spacing). Such ensembles enable us to study atmospheric processes characteristic for complex terrain in unprecedented detail.
In this work, we use three km-scale datasets: the dynamically downscaled, convection-permitting CORDEX-FPS ensemble on convective phenomena over the greater Alpine region, and two statistically downscaled and bias-adjusted datasets used in the national climate scenarios of Austria and Switzerland (OeKS15 and CH2018, respectively). For comparison, we also analyze the three coarser-resolution ensembles from which these km-scale ensembles were downscaled. We assess to what degree these different ensembles are able to capture various daily precipitation indices, and their dependence on temperature and elevation. We discuss how credible these datasets are when evaluated against observations, and we examine how the precipitation characteristics are projected to change in the warming climate.
Our findings show that the spatial patterns of the analyzed precipitation indices are fairly similar among the km-scale ensembles in the evaluation period. However, we find differences between the datasets at low temperatures - compared to observations, the dynamically downscaled ensemble strongly overestimates daily precipitation intensity and frequency, whereas the bias-adjusted datasets underestimate these. The dynamically downscaled ensemble also shows biases at high elevations. In the climate change projections, we see notable season-dependent differences between the datasets, and some of the bias-adjusted models exhibit spurious signals.
How to cite: Medvedova, A., Kohlhauser, I., Maraun, D., Rotach, M. W., and Ban, N.: Precipitation and Its Future Changes in the Greater Alpine Region: High-resolution Bias-adjusted Versus Dynamically Downscaled Datasets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3125, https://doi.org/10.5194/egusphere-egu26-3125, 2026.
Wind storms are known to cause substantial impacts on human safety and infrastructure across Europe. Additionally, climate projections using regional climate models indicate that wind extremes may increase over Europe under climate change (Outten and Sobolowski, 2021). Despite its socio-economic importance, however, the representation of wind extremes in models remains underexplored. One of the reasons for this is the lack of reliable long-term observations for model validation leading to the use of reanalysis as a reference dataset even though the reanalysis biases in near-surface wind speeds are well documented.
We compare the EURO-CORDEX multi-model ensemble and ERA5 reanalysis against the quality-controlled observational EuSWiO dataset (Rojas‐Labanda et al., 2023). More specifically, we extract return levels from extreme winds from the daily maximum or daily average wind speed for 130 station locations in and around Belgium. While, as expected, ERA5 shows improved temporal correlations, it suffers from a systematic underestimation of high wind extremes while the EURO-CORDEX ensemble median produces return levels closer to EuSWiO. This gives confidence in the use of regional climate model ensembles for climate-change projections and advocates caution in the use of reanalyses for extreme winds.
- Rojas‐Labanda et al. (2023). Surface wind over Europe: Data and variability. International Journal of Climatology, 43(1), 134-156.
- Outten, S., and Sobolowski, S. (2021). Extreme wind projections over Europe from the Euro-CORDEX regional climate models. Weather and Climate Extremes, 33, 100363.
How to cite: Stanus, R., Carette, N., Van de Vyver, H., and Van Schaeybroeck, B.: The evaluation of wind extremes from regional climate models and reanalysis over Belgium, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5212, https://doi.org/10.5194/egusphere-egu26-5212, 2026.
High-impact mid-latitude cyclones affecting Central Europe are shaped not only by synoptic-scale circulation but also by thermodynamic boundary conditions, including sea surface temperature (SST) anomalies that influence moisture availability and storm intensity. Understanding the sensitivity of such events to SST conditions is important for interpreting recent extreme weather events in a warming climate. Here, we apply a storyline-based modelling framework to investigate the role of SST anomalies in Storm Boris, a severe cyclone that affected Central Europe, including Vienna, in September 2024. Convection-permitting simulations are performed with the ICON model at 3 km horizontal resolution over the EURO-CORDEX domain for a one-month period encompassing the event, with the atmospheric initial and lateral boundary conditions provided by the ERA5 reanalysis. A control simulation is driven by observed 2024 boundary conditions, while a sensitivity experiment replaces all SSTs with a 1981–2010 climatological mean, thereby removing the influence of SST anomalies while preserving the large-scale atmospheric circulation associated with the cyclone. By comparing these simulations, we examine how SST anomalies affect cyclone development, moisture transport, precipitation intensity, and storm structure over Central Europe. Rather than providing a probabilistic attribution, this storyline approach explores physically plausible alternative realization of the same circulation pattern under different SST state. While simulations and analyses are ongoing, this contribution presents initial results and methodological insights into the application of convection-permitting storyline experiments to assess the thermodynamic sensitivity of extreme mid-latitude cyclones, with implications for understanding high-impact European weather events.
How to cite: Batibeniz, F., Messmer, M., Zeman, C., and Raible, C. C.: A storyline-based ICON experiment to assess the role of sea surface temperature anomalies in Storm Boris, a high-impact Central European cyclone, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5520, https://doi.org/10.5194/egusphere-egu26-5520, 2026.
Irrigation exerts contrasting effects on humid heat stress by simultaneously inducing surface cooling and increasing atmospheric humidity through enhanced evapotranspiration, leading to heated debates in heavily irrigated regions such as the North China Plain (NCP). Intensifying or alleviating heat stress due to irrigation may become increasingly important in the context of ongoing global warming. Moreover, irrigation progressively alters vegetation growth, further modifying the local climate and amplifying uncertainties in assessing its overall impact. However, most previous studies have assumed static vegetation or relied on prescribed satellite-based vegetation data, overlooking the two-way feedbacks between irrigation and vegetation. This study adopts recently-improved dynamic crop module that considers the indirect irrigation effect from vegetation, and incorporates it into the Weather Research and Forecasting Model (WRF), to explore the irrigation effects on humid heat stress over the NCP. Using this enhanced model, the EC-Earth3 global projection, which shows a medium level of warming sensitivity among available CMIP6 models, is dynamically downscaled for historical and future periods. The widely used heat stress index, the Wet-Bulb Globe Temperature (WBGT), is then calculated to quantify the combined effects of temperature and humidity changes in response to irrigation amid ongoing global warming. The analysis focuses on understanding the role of dynamic vegetation feedbacks in modulating future humid heat in regions with intense irrigation. Our findings will provide valuable insights into effective irrigation management strategies for thermal risk mitigation.
[Acknowledgment]
This study was supported by the Research Program for Agricultural Science & Technology Development (Project No. RS-2025-02214912), funded by the National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.
How to cite: Shen, H., Fan, Y., Hur, J., and Im, E.-S.: Irrigation effects on humid heat stress under global warming : Focusing on dynamic vegetation feedbacks in a regional climate model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6245, https://doi.org/10.5194/egusphere-egu26-6245, 2026.
The German research projects NUKLEUS (Actionable local climate information for Germany) and UDAG (Updating the data basis for adaptation to climate change in Germany) in collaboration with the CLM Community recently generated new RCM ensembles from downscaling of CMIP6 GCMs for EURO-CORDEX (at 12km) and additionally on the convection-permitting scale (3km) for Central Europe. The NUKLEUS ensemble consists of three GCMs from the EURO-CORDEX Balanced Ensemble Matrix (BEM) initiative, each dynamically downscaled by three RCM (ICON-CLM, COSMO CLM and REMO) for the ssp370 scenario. The UDAG ensemble encompasses a downscaling with just ICON-CLM, but for all six BEM GCMs and multiple ssp-scenarios.
In this contribution, we evaluate the NUKLEUS ensemble to assess the propagation of biases and the consistency of climate change signals along different GCM-RCM models chain. Biases are examined through analyses of individual components of the hydrological cycle, which plays a central role in land–atmosphere flux interactions and whose realistic representation is crucial for reliable climate projections, alongside near-surface air temperature and sensible heat fluxes. ERA5 is used as a reference to explore model behavior over both land and ocean. The analysis is conducted on the EUR-12 grid over Europe in winter and summer, with detailed focus on Central Europe and the Mediterranean.
Regarding biases, results show partial consistency between GCMs and RCMs. RCMs generally provide a more consistent representation of the hydrological cycle than GCMs. However, given large deviations of GCM forcing data from observations, e.g., sea surface temperature, can cause RCMs to adjust surface fluxes, either reducing inherited biases or generating new ones. These adaptations occur regionally, especially in MIROC6-driven simulations over the Mediterranean.
Regarding the climate change signal, the biases affect the representation of the hydrological cycle, with the strongest impact in the Mediterranean. Here, MIROC6-driven simulations show an intensification of the hydrological cycle particularly pronounced in future projections. Despite such model-specific issues, RCMs generally produce narrower climate change signals than their driving GCMs.
These results highlight that the optimal GCM selection for downscaling is region-specific and that GCM–RCM model chains should be analysed with caution in ocean-influenced areas.
How to cite: Cusinato, E., Feldmann, H., Geyer, B., Ludwig, P., Trachte, K., and Pinto, J. G.: From Global to Regional Climate Models: Consistency assessment of the hydrological cycle, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14246, https://doi.org/10.5194/egusphere-egu26-14246, 2026.
Regional Climate Models (RCMs) were used to simulate the climate of Ireland (1980-2100). The simulations were run at high spatial resolution (4km), allowing a more realistic representation of important physical processes and enabling a more accurate evaluation of the local impacts. To address the uncertainty inherent in climate model projections, three RCMs (COSMO-CLM, WRF and HCLIM) were used to dynamically downscale outputs from an ensemble of CMIP6 GCMs (CMCC-CM2-SR5, CNRM-ESM2-1, MIROC6, MPI-ESM1-2-HR, NorESM2-MM, ECEarth3-Veg and ECEarth3). The outer domain (used to drive the inner 4km domain) was run at 12km (COSMO-CLM) and 20km (WRF) grid spacings, and roughly corresponds to the Euro-CORDEX 12km domain. To account for the uncertainty in future global emissions, the future climate was simulated under all four tier-1 SSP–RCP (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) emission scenarios. The resulting large ensemble size is essential for a more accurate quantification of climate change uncertainty.
The RCM configurations were validated by running simulations of the past Irish climate for the period 1980–2010, driven by both fifth-generation ECMWF atmospheric reanalysis of the global climate (ERA5) and the CMIP6 ESM datasets, and comparing the output against observational data.
In addition to the standard climate fields (e.g., temperature, precipitation, wind, snowfall, humidity and radiation), the research provides projections of additional derived variables such as wind power at turbine height, photovoltaic power, evapotranspiration and Universal Thermal Climate Index (UTCI).
The scenario-based projections are supplemented with global warming threshold scenario projections for temperature and precipitation.
How to cite: Nolan, P., Hanley, J., Todt, M., and Semmler, T.: High-Resolution Climate Projections for Ireland - An RCM-CMIP6 Multi-Model Ensemble, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14386, https://doi.org/10.5194/egusphere-egu26-14386, 2026.
Simulating precipitation over South America remains difficult because rainfall is tightly controlled by interactions between large-scale circulation and the Andes, whose north–south barrier strongly shapes moisture transport from the Amazon toward southern South America. In the southern tropics, this circulation creates precipitation “hotspots” east of the mountains, needing high-resolution modeling to be correctly represented. This study assesses how well SENAMHI-Peru’s dynamically downscaled regional climate simulations reproduce the key atmospheric circulation patterns linked to continental precipitation, by comparing historical seasonal fields (1981–2014) from both the driving global models (GCMs) and the regional simulations (RCMs) against ERA5 reanalysis. The regional simulations were generated using the Weather Research and Forecasting (WRF) model forced by two CMIP6 global climate models: MPI-ESM1.2-LR-ENS, which includes bias correction prior to downscaling, and NorESM2-MM, which does not include bias correction but demonstrates skill in representing large-scale atmospheric patterns over South America. Using a broad set of diagnostics, the analysis evaluates the representation of major features such as the South American Low-Level Jet, Hadley cell structure, ITCZ position, and local circulation effects. Model–data differences are traced mainly to how topography is represented and to physical parameterizations, providing guidance for improving SENAMHI-Peru’s regionalization protocol ahead of future downscaling experiments.
How to cite: Aliaga Nestares, V., Khodri, M., Clementine, J., Jácome Vergaray, G., and Llacza Rodriguez, A.: From GCM to RCM: How Well Does Dynamical Downscaling Reproduce Southern Tropical South American Moisture Transport and Rainfall?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13236, https://doi.org/10.5194/egusphere-egu26-13236, 2026.
Climate change represents a major threat to rainfed agriculture and food security, particularly in tropical regions that are highly dependent on the climate, especially rainfall. In sub-Saharan Africa, rising temperatures and increasing rainfall variability further exacerbate the vulnerability of agricultural systems. In the Sudano–Sahelian zone of Cameroon (>8° latitude), where maize (Zea mays L.) is a key staple crop, this strong climatic dependence exposes crop yields to pronounced interannual variability. In this context, assessing the future impacts of climate change using crop models driven by climate projections derived from IPCC scenarios is important for anticipating agricultural risks.
This study aims to produce gridded simulations of attainable maize yields under rainfed conditions and current management assumptions and under the RCP 8.5 climate scenario in the Sudano–Sahelian region of Cameroon for the period 2026–2055. To this end, the SARRA-Py crop model (DOI: 10.1051/cagri/2025018), designed for tropical agricultural systems, was calibrated using two complementary data sources. First, biophysical data from a field experiment conducted in Langui (Northern Cameroon) during the 2023 and 2024 growing seasons were used to define the intervals for parameter values for phenological stages, specific leaf area (SLA), and the potential yield coefficient. Second, Bayesian optimization of these parameters was performed with the objective of increasing the Pearson correlation coefficient between simulated yields and observed zonal yields (10 divisions × 24 years - 1999–2022 period) at the Sudano-Sahelian scale in Cameroon. Using these calibrated parameters, yield simulations were then forced by climate projections from an ensemble of ten corrected global and regional climate model combinations from the CORDEX-CORE framework (DOI: 10.5281/zenodo.17054199), adjusted using two bias-correction methods (CDF-t and ISIMIP) against rainfall observed through a regional rain gauge network (DOI: 10.5281/zenodo.11067784), and minimum and maximum temperature time series over the region derived from reanalysis datasets (DOI: 10.24381/cds.6c68c9bb). Temporal trends in simulated yields were analysed using the Mann–Kendall trend test and Sen’s slope estimator.
SARRA-Py satisfactorily reproduces the interannual and spatial variability of historically observed maize yields, with a significant correlation (r = 0.6; p < 0.001). Climate model projections then converge toward a prevailing decline in simulated maize yields across most of the Sudano-Sahelian zone of Cameroon over the 2026–2055 period under the RCP 8.5 scenario. Both bias-correction methods project annual yield reductions of approximately 1–2% per year in most of the southern and northeastern parts of the study area. The magnitude and spatial coherence of trends vary across the model ensemble, with ISIMIP generally showing more spatially homogeneous and slightly weaker negative signals than CDF-t. Overall, despite pronounced spatial heterogeneity, these projections indicate a deterioration of maize production potential and an increased vulnerability of rainfed agricultural systems under the considered scenario. These findings highlight the need for targeted adaptation strategies to enhance the resilience of agricultural systems in the Sudano-Sahelian of Cameroon.
Keywords : Northern Cameroon, RCP 8.5, SARRA-Py crop model, maize, yield; climate change
How to cite: Nenwala, V. H., Njouenwet, I., Gonne, S., Aoudou Doua, S., and Lavarenne, J.: Predominant decline in rainfed maize yield potential by 2055 under RCP8.5 in Sudano–Sahelian Cameroon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13397, https://doi.org/10.5194/egusphere-egu26-13397, 2026.
Arctic climate projections are characterized by pronounced uncertainty, stemming mainly from structural uncertainties related to the representation of Arctic processes and feedbacks, including those associated with permafrost, cryosphere–atmosphere coupling, and sea ice. Within the PolarRES project’s framework, we apply a storyline-based approach to address parts of these uncertainties and investigate how different physically plausible Arctic futures manifest in high-resolution regional climate simulations. We analyze an ensemble of five regional climate models (RCMs) at 11 km resolution over the Arctic, each driven by two CMIP6 global climate models representing contrasting storylines of Arctic change: CNRM-ESM2-1 and NorESM2-MM, under the high-emission scenario SSP3-7.0. The two driving GCMs differ in their representation of Arctic climate change mechanisms, with NorESM2-MM exhibiting stronger lower-tropospheric Arctic amplification and CNRM-ESM2-1 showing comparatively weaker atmospheric amplification but enhanced surface warming in the Barents–Kara Sea region.
Climate change signals are assessed by comparing end-of-century conditions (2070–2099) to a present-day reference period (1985–2014) for near-surface 2 m air temperature, total precipitation, and the seasonal number of freezing (ice) days. Across all variables, the RCMs broadly reproduce the large-scale spatial patterns imposed by their driving GCMs, but also introduce pronounced regional modifications and inter-model spread, particularly over the ice covered parts of the Arctic ocean.
The strongest warming occurs in winter, exceeding 15 K over parts of the Arctic Ocean, with several RCMs amplifying the Barents–Kara Sea warming relative to the driving models. Summer warming is comparatively weak and consistent across both storylines, whereas spring and autumn exhibit enhanced inter-RCM variability, pointing to sensitivities in snow-albedo feedbacks and melt–freeze processes. Changes in freezing days reveal substantial ensemble spread in summer, despite similar mean temperature change signals, highlighting the nonlinear dependence of threshold-based metrics on absolute temperature levels.
Projected precipitation increases are largest in winter and autumn, particularly over the Arctic Ocean and the Barents–Kara region, with relative increases often exceeding 80–100%. While overall patterns resemble those of the driving GCMs, individual RCMs exhibit notable deviations, especially over sea-ice loss regions.
These results demonstrate that regional climate models add important, physically meaningful structure to Arctic climate change signals, emphasizing the role of regional processes in shaping plausible future Arctic climates within a storyline framework.
How to cite: Matthes, H., Mooney, P., de Falco, C., Mottram, R., Landwehrs, J., Rinke, A., Rambin, C., Fettweis, X., van de Berg, W. J., van Dalum, C., and Landgren, O.: Contrasting Arctic Climate Change Patterns from Storyline-Driven Regional Climate Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16757, https://doi.org/10.5194/egusphere-egu26-16757, 2026.
High-resolution climate change projections are essential for assessing Arctic climate impacts and extreme events. Such projections are commonly obtained through statistical downscaling, dynamical downscaling, regional refinement of global climate models (GCMs), or other emerging high-resolution modelling frameworks. While dynamical methods allow physically consistent regional projections based on global forcing, they remain computationally expensive, limiting the number of GCMs and scenarios that can be explored. As a result, structural uncertainty associated with modelling strategy, choice of driving GCM, and emission scenario is often insufficiently quantified, particularly for Arctic climate change signals. A key challenge is that systematic comparisons of climate change signal patterns across different high-resolution modelling approaches are rare. This is partly because few modelling systems allow consistent simulations in global, regionally refined, and limited-area configurations using the same model physics. Consequently, it remains unclear to what extent differences in projected Arctic climate change arise from large-scale forcing versus the downscaling framework itself.
Here, we address this gap using the ICON (ICOsahedral Nonhydrostatic) modelling system in three complementary configurations: a global setup with uniform coarse resolution (~53 km), a globally variable-resolution configuration with enhanced Arctic resolution (~13 km) and coarse resolution elsewhere (~53 km), and an Arctic high-resolution limited-area mode (~11 km). Control and scenario simulations are available for multiple driving GCMs and emission pathways, enabling a targeted, storyline-based assessment of structural uncertainty, focusing on physically consistent responses to prescribed large-scale forcings. This experimental design allows us to disentangle uncertainties related to (i) the downscaling approach, (ii) the choice of large-scale forcing, and (iii) the emission scenario, while keeping model formulation consistent. We analyse Arctic climate change signals for near-surface air temperature and precipitation, focusing on seasonal mean responses. Pattern-based, multi-simulation comparisons are used to assess agreement and differences across simulations at both pan-Arctic and regional scales. This allows us to identify aspects of Arctic climate change that are robust across downscaling strategies, as well as regions where projected responses are particularly sensitive to model configuration or large-scale forcing, highlighting areas of enhanced structural uncertainty. Our results provide guidance for interpreting high-resolution Arctic climate projections and support targeted model selection for impact-oriented studies and regional climate assessments.
How to cite: Köhler, R., Matthes, H., Seland Graff, L., Zheng, Q., Krishnan, S. R. R., Richter, K., Landwehrs, J., and Handorf, D.: Same model, different answers? Structural uncertainty in Arctic climate change across downscaling approaches, forcings, and scenarios , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18347, https://doi.org/10.5194/egusphere-egu26-18347, 2026.
A reliable assessment of Impact, Adaptation, and Vulnerability of a region to climate change requires a high resolution climate information, particularly for regions with complex terrain. The North East Region (NER) of India, bounded by the eastern Himalayas in the north and the Bangladesh floodplains and Bay of Bengal in the south, is one such region. Several studies have identified NER as highly vulnerable to global warming, with six of its eight Indian states classified under high to moderate vulnerability. Dynamical downscaling for such regions necessitates the selection of a reliable Global Climate Model (GCM) and effective correction of its inherent biases. This study evaluates precipitation and 2 m air temperature from 16 GCMs available in the Coupled Model Intercomparison Project Phase 6 (CMIP6) over NER using eight performance metrics: Jensen–Shannon distance, mean absolute error, percentage bias, mutual information, correlation, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and root mean square error. These metrics collectively capture different aspects of model skill. The GCMs are subsequently ranked using two multi-criteria decision-making (MCDM) approaches: VIKOR and TOPSIS, both based on distance from an ideal solution but differing in their optimization philosophies. VIKOR and TOPSIS both use distance from ideal solution to rank with TOPSIS preferring the alternative closest to the ideal solution and VIKOR finding the alternative with the maximum group utility of the criterion and minimum individual regret. The ranking from both VIKOR and TOPSIS indicates MPI-ESM-1-2-HR and EC-Earth-Veg as the most reliable models annually and during monsoons over NER. In addition, the study also assessed a trend-preserving bias-correction framework for generating reliable initial and boundary conditions for regional dynamical downscaling, using MPI-ESM-1-2-HR as the primary driver. The method decomposes the climate time series into a non-linear trend and a perturbation component, with variance bias correction applied to the perturbations assuming the variance bias to be same for the future scenarios. To address uncertainty in long-term mean from single GCM simulation, the bias in trend is corrected for the multi-model ensemble (MME) mean trend, derived from all 16 GCMs. Singular Spectrum Analysis (SSA) is employed to extract the non-linear trend due to its strong mathematical foundation and orthogonality properties. Preliminary results demonstrate the method’s effectiveness in correcting both long-term trend and variance biases, supporting its suitability for regional climate downscaling over NER.
How to cite: Chakravorty, A., Kundu, S. S., Gogoi, R. B., and Aggarwal, S. P.: MCDM-Based Ranking and Trend-Preserving Bias Correction of CMIP6 Models for Regional Downscaling over Northeast India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19093, https://doi.org/10.5194/egusphere-egu26-19093, 2026.
The Euro-CORDEX initiative (https://www.euro-cordex.net/) coordinates regional climate simulations (RCMs) over Europe, offering a unique opportunity to study regional climate at high-resolution scales (10-12 km). In the overall procedure of regional climate modelling future scenarios, as a first step, the RCMs are forced by the ERA5 reanalysis over a common period (1980-2020). These simulations allows to inspect their ability to describe the different current regional european climates from the wind perspective, both the most robust and the more uncertain aspects, when compared with the available observations and higher-resolution reanalyses (such as CERRA). The wind field, particularly at the surface, has not been evaluated in detail for these ensembles of simulations as much as temperature or precipitation fields. Annual, seasonal mean fields, annual or daily cycles, as well as more regional statistics associated with orographic or land/sea contrast aspects are studied, including named-known local-to-regional winds. Around 16 RCM simulations are already available, that allows for a complete analysis of these features related to wind description. These analyses also aim to include the inspection of wind energy resources, related to the availability of adequate data in frequency and vertical resolution from the RCM ensemble.
How to cite: Sanchez, E., Molina, M. O., Gutierrez, C., Ortega, M., and Lopez-Franca, N. and the EuroCORDEX modelling community: A first analysis of surface wind as seen from Euro-CORDEX regional climate modelling ensemble ERA5-forced (1980-2020) simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20689, https://doi.org/10.5194/egusphere-egu26-20689, 2026.
The broader Euro-Mediterranean region, encompassing most of Europe as well as parts of North Africa and the Middle East, has been identified by the IPCC as one of the fastest-warming regions globally. At the same time, it has experienced a marked increase in the frequency and intensity of extreme weather events, including heatwaves and droughts. Reliable regional climate information is therefore essential for understanding ongoing changes and supporting climate impact assessments. Within the framework of the World Climate Research Programme (WCRP), CORDEX provides coordinated regional climate model (RCM) simulations to advance regional climate downscaling and its applications. Over the past two decades, it has generated standardized datasets to support analyses of present and future climate extremes, trends, and impacts on society, ecosystems, and agriculture across fourteen continental-scale domains, covering nearly all global land areas. Following the CMIP5-driven CORDEX simulations used in the latest IPCC Assessment Report, the newly launched CMIP6-driven CORDEX initiative is expected to further populate community data repositories and contribute to the next IPCC assessment. In the present study, we evaluate historical simulations over the EURO-CORDEX domain, obtained by dynamically downscaling the CMIP6 EC-Earth3-Veg Earth system model using the Weather Research and Forecasting (WRF) model. The simulations follow the CORDEX-CORE Phase II experimental protocol and model configuration. Model performance is assessed against the E-OBS v30.0e daily gridded observational dataset. The evaluation focuses on key near-surface meteorological variables, including temperature and precipitation, examining spatial patterns, aspects of extreme weather, seasonal variability, and systematic biases.
How to cite: Gavrouzou, M. and Zittis, G.: Dynamical downscaling of EC-Earth3-Veg over the EURO-CORDEX domain with WRF: A contribution to CORDEX-CORE Phase II, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23171, https://doi.org/10.5194/egusphere-egu26-23171, 2026.
While machine learning (ML) weather models are emerging as promising tools for predicting global weather conditions, their coarse resolution and systematic biases restrict proper application in regional contexts. This study assesses the added value of dynamical downscaling and the effectiveness of model output statistics in post-processing to enhance July temperature predictions in South Korea from FuXi-ENS, a state-of-the-art ML-based global forecast, with a one-month lead time. To improve the performance of FuXi-ENS in this region characterized by complex geographic features, dynamical downscaling is conducted using the Weather Research and Forecasting modeling system optimized for the target region. A Joint Gaussian model is then applied to post-process the downscaled predictions and is benchmarked against widely used quantile mapping under the framework of leave-one-year-out cross-validation. Despite significant improvements in the spatial representation of temperature compared to the FuXi-ENS ensemble, the downscaled predictions still exhibit large errors in temporal evolution, often underperforming relative to reference climatological forecasts. This study clearly demonstrates that further improvements based on model output statistics could enhance the accuracy of these predictions. Consequently, it substantiates the synergetic integration of dynamical downscaling with statistical post-processing to transform ML-based global predictions into actionable regional information.
Acknowledgments
This work was supported by Korea Environmental Industry & Technology Institute through Water Management Program for Drought Project, funded by Korea Ministry of Environment (2022003610003).
How to cite: Huang, Z., Im, E.-S., Ha, S., Shen, H., and Kwon, H.-H.: Regionalization of FuXi-ENS global predictions in South Korea through dynamical downscaling and model output statistics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2769, https://doi.org/10.5194/egusphere-egu26-2769, 2026.
Recent summer power outages in China highlight the vulnerability of energy systems to compound hot-dry events (CHDE). These events strain power grids by increasing cooling demand while reducing renewable generation, particularly from hydropower. As China transitions toward a weather-dependent renewable energy system, understanding CHDE impacts becomes crucial for ensuring system resilience. However, current research on climate-energy interactions relies primarily on coarse global climate models and simplified empirical equations, potentially obscuring critical local-scale dynamics and technoeconomic factors relevant to adaptation planning. While high-resolution climate models better represent regional climate patterns, their advantages for energy system planning remain largely unexplored.
This study investigates how climate change-induced CHDE affects power generation and demand in Southern China, with particular focus on evaluating high-resolution downscaling approaches for energy planning. We validate two downscaling frameworks: a Generative Adversarial Network (GAN)-based statistical model and a dynamical regional climate model, by reproducing recent blackout events in 2022. We then apply both methods to downscale projections from MPI-ESM1-2-HR under SSP2-4.5 and SSP3-7.0 scenarios, generating 4-km, hourly resolution climate data for the near-term (2041–2060) and long-term (2081–2100) periods. Using these downscaled outputs, we estimate renewable power generation potential and temperature-driven electricity demand. We systematically quantify uncertainties arising from downscaling method choice and emission scenarios. Our findings demonstrate that climate data resolution significantly influences energy system planning outcomes and that rigorous uncertainty characterization across modeling chains is essential for robust climate impact assessments.
[Acknowledgement]
This research was supported by Research Grants Council of Hong Kong through Theme-based Research Scheme (T31-603/21-N) and General Research Fund (GRF16308722).
How to cite: Zhou, Z., Lin, H., Jiang, H., Im, E.-S., and McElroy, M. B.: Does downscaling method matter? Assessing compound hot-dry event impacts on renewable power in Southern China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15651, https://doi.org/10.5194/egusphere-egu26-15651, 2026.
