Klaus Keuler

This study presents the evaluation of the historical reference simulations (1961–1990) of the NUKLEUS ensemble, the first kilometer-scale convection-permitting multi-model climate ensemble for Germany. The main goal is to examine to what extent these high-resolution simulations can provide high-quality and actionable information for climate adaptation measures in Germany. The NUKLEUS ensemble comprises nine members, generated by dynamically downscaling three global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) with three regional climate models to a 3 km grid over a Central European domain. The evaluation focuses on the spatio-temporal and statistical representation of basic meteorological variables (temperature, wind speed, and precipitation) and derived application-relevant climate indices compared to reanalyses and observation-based data sets. The analyses are performed for Germany and six pilot regions representing diverse climatic and physiographic settings. The results reveal that the ensemble overall exhibits moderate biases with temperature and precipitation generally showing high distributional skill. Only few simulations exhibit strong warm biases, particularly during summer, while all simulations exhibit a wet bias throughout the year, except local dry biases during summer in individual members. Wind biases are more heterogeneous, particularly due to reference data constraints over complex terrain. Percentile-based climate indices are well reproduced, while fixed-threshold indices show systematic deviations. A comparison with previous regional climate model ensembles highlights the added value of the here-chosen multi-model approach. With regard to downstream applications, a quantile (delta) mapping bias correction is applied to daily precipitation totals and daily mean, minimum, and maximum temperature, which removes climatological biases and markedly improves threshold-based indices. The paper also demonstrates important limitations of this bias correction approach for event-based applications, showing that it may disrupt spatial coherence and introduce spurious spatial gradients in precipitation fields, which can affect the characterization of extreme precipitation events. Overall, the presented analyses support the use of the NUKLEUS ensemble as a high-resolution basis for regional climate and impact studies, while underlining that application-oriented post-processing and validation should be tailored to the target variable and use case.

This study presents the evaluation of the historical reference simulations (1961–1990) of the NUKLEUS ensemble, the first kilometer-scale convection-permitting multi-model climate ensemble for Germany. The main goal is to examine to what extent these high-resolution simulations can provide high-quality and actionable information for climate adaptation measures in Germany. The NUKLEUS ensemble comprises nine members, generated by dynamically downscaling three global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) with three regional climate models to a 3 km grid over a Central European domain. The evaluation focuses on the spatio-temporal and statistical representation of basic meteorological variables (temperature, wind speed, and precipitation) and derived application-relevant climate indices compared to reanalyses and observation-based data sets. The analyses are performed for Germany and six pilot regions representing diverse climatic and physiographic settings. The results reveal that the ensemble overall exhibits moderate biases with temperature and precipitation generally showing high distributional skill. Only few simulations exhibit strong warm biases, particularly during summer, while all simulations exhibit a wet bias throughout the year, except local dry biases during summer in individual members. Wind biases are more heterogeneous, particularly due to reference data constraints over complex terrain. Percentile-based climate indices are well reproduced, while fixed-threshold indices show systematic deviations. A comparison with previous regional climate model ensembles highlights the added value of the here-chosen multi-model approach. With regard to downstream applications, a quantile (delta) mapping bias correction is applied to daily precipitation totals and daily mean, minimum, and maximum temperature, which removes climatological biases and markedly improves threshold-based indices. The paper also demonstrates important limitations of this bias correction approach for event-based applications, showing that it may disrupt spatial coherence and introduce spurious spatial gradients in precipitation fields, which can affect the characterization of extreme precipitation events. Overall, the presented analyses support the use of the NUKLEUS ensemble as a high-resolution basis for regional climate and impact studies, while underlining that application-oriented post-processing and validation should be tailored to the target variable and use case.

This study presents the evaluation of the historical reference simulations (1961–1990) of the NUKLEUS ensemble, the first kilometer-scale convection-permitting multi-model climate ensemble for Germany. The main goal is to examine to what extent these high-resolution simulations can provide high-quality and actionable information for climate adaptation measures in Germany. The NUKLEUS ensemble comprises nine members, generated by dynamically downscaling three global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) with three regional climate models to a 3 km grid over a Central European domain. The evaluation focuses on the spatio-temporal and statistical representation of basic meteorological variables (temperature, wind speed, and precipitation) and derived application-relevant climate indices compared to reanalyses and observation-based data sets. The analyses are performed for Germany and six pilot regions representing diverse climatic and physiographic settings. The results reveal that the ensemble overall exhibits moderate biases with temperature and precipitation generally showing high distributional skill. Only few simulations exhibit strong warm biases, particularly during summer, while all simulations exhibit a wet bias throughout the year, except local dry biases during summer in individual members. Wind biases are more heterogeneous, particularly due to reference data constraints over complex terrain. Percentile-based climate indices are well reproduced, while fixed-threshold indices show systematic deviations. A comparison with previous regional climate model ensembles highlights the added value of the here-chosen multi-model approach. With regard to downstream applications, a quantile (delta) mapping bias correction is applied to daily precipitation totals and daily mean, minimum, and maximum temperature, which removes climatological biases and markedly improves threshold-based indices. The paper also demonstrates important limitations of this bias correction approach for event-based applications, showing that it may disrupt spatial coherence and introduce spurious spatial gradients in precipitation fields, which can affect the characterization of extreme precipitation events. Overall, the presented analyses support the use of the NUKLEUS ensemble as a high-resolution basis for regional climate and impact studies, while underlining that application-oriented post-processing and validation should be tailored to the target variable and use case.

This study presents the evaluation of NUKLEUS, the first kilometre-scale, multi-model convection-permitting regional climate ensemble (CPM) for Germany. Three state-of-the-art regional climate models (ICON-CLM, COSMO-CLM, and REMO) were run at ~3 km horizontal resolution using a two-step downscaling chain driven by ERA5 reanalysis. The ensemble provides high-resolution climate information for Central Europe. In particular, we evaluate the CPM simulation results for Germany with a focus on six representative pilot regions selected within the German RegIKlim programme. Temperature, precipitation, global radiation, and near-surface wind, including their spatial patterns, annual and diurnal cycles, distributional characteristics, are compared to observational datasets. Moreover, selected climate indices relevant for heat and precipitation extremes are analysed. Overall, the ensemble demonstrates substantial added value compared to coarser-scale regional climate modelling, particularly in capturing regional climatic features and fine-scale variability. Temperature is reproduced with small biases (mostly within ± 0.5 K), with ICON-CLM performing best, while COSMO-CLM shows a weak cold bias and REMO a warm bias in parts of southern Germany. The CPM models realistically capture daily temperature distributions, though REMO underestimates minimum-temperature extremes. The annual cycle of precipitation is generally well represented, but all CPM models tend to overestimate totals in several regions, e.g., REMO exhibits a distinct spatial bias pattern with stronger deviations along topographic gradients. Extreme precipitation frequencies are generally overestimated, while regional contrasts such as stronger extremes in mountainous regions are preserved. Diurnal cycles show deficiencies specific to the models, including timing errors of afternoon precipitation peaks and misrepresentation of nocturnal precipitation revivals. For global radiation, ICON-CLM achieves the smallest biases, benefiting from its modern radiation scheme (ecRad), whereas COSMO-CLM and REMO show region specific over- and underestimations linked to cloud representation. 10 m wind speed diurnal cycles are best simulated by ICON-CLM, which captures the nocturnal wind minimum, while COSMO-CLM and REMO generally overestimate nighttime wind. Climate indices reveal underestimation of heat-related metrics (summer days and hot days) and systematic overestimation of heavy precipitation indices, although spatial patterns and regional differences are reproduced. In summary, we conclude that NUKLEUS provides valuable climate information for Germany, supporting climate-impact assessments and adaptation planning at municipal to regional scales.

This study presents the evaluation of NUKLEUS, the first kilometre-scale, multi-model convection-permitting regional climate ensemble (CPM) for Germany. Three state-of-the-art regional climate models (ICON-CLM, COSMO-CLM, and REMO) were run at ~3 km horizontal resolution using a two-step downscaling chain driven by ERA5 reanalysis. The ensemble provides high-resolution climate information for Central Europe. In particular, we evaluate the CPM simulation results for Germany with a focus on six representative pilot regions selected within the German RegIKlim programme. Temperature, precipitation, global radiation, and near-surface wind, including their spatial patterns, annual and diurnal cycles, distributional characteristics, are compared to observational datasets. Moreover, selected climate indices relevant for heat and precipitation extremes are analysed. Overall, the ensemble demonstrates substantial added value compared to coarser-scale regional climate modelling, particularly in capturing regional climatic features and fine-scale variability. Temperature is reproduced with small biases (mostly within ± 0.5 K), with ICON-CLM performing best, while COSMO-CLM shows a weak cold bias and REMO a warm bias in parts of southern Germany. The CPM models realistically capture daily temperature distributions, though REMO underestimates minimum-temperature extremes. The annual cycle of precipitation is generally well represented, but all CPM models tend to overestimate totals in several regions, e.g., REMO exhibits a distinct spatial bias pattern with stronger deviations along topographic gradients. Extreme precipitation frequencies are generally overestimated, while regional contrasts such as stronger extremes in mountainous regions are preserved. Diurnal cycles show deficiencies specific to the models, including timing errors of afternoon precipitation peaks and misrepresentation of nocturnal precipitation revivals. For global radiation, ICON-CLM achieves the smallest biases, benefiting from its modern radiation scheme (ecRad), whereas COSMO-CLM and REMO show region specific over- and underestimations linked to cloud representation. 10 m wind speed diurnal cycles are best simulated by ICON-CLM, which captures the nocturnal wind minimum, while COSMO-CLM and REMO generally overestimate nighttime wind. Climate indices reveal underestimation of heat-related metrics (summer days and hot days) and systematic overestimation of heavy precipitation indices, although spatial patterns and regional differences are reproduced. In summary, we conclude that NUKLEUS provides valuable climate information for Germany, supporting climate-impact assessments and adaptation planning at municipal to regional scales.

This study presents the evaluation of NUKLEUS, the first kilometre-scale, multi-model convection-permitting regional climate ensemble (CPM) for Germany. Three state-of-the-art regional climate models (ICON-CLM, COSMO-CLM, and REMO) were run at ~3 km horizontal resolution using a two-step downscaling chain driven by ERA5 reanalysis. The ensemble provides high-resolution climate information for Central Europe. In particular, we evaluate the CPM simulation results for Germany with a focus on six representative pilot regions selected within the German RegIKlim programme. Temperature, precipitation, global radiation, and near-surface wind, including their spatial patterns, annual and diurnal cycles, distributional characteristics, are compared to observational datasets. Moreover, selected climate indices relevant for heat and precipitation extremes are analysed. Overall, the ensemble demonstrates substantial added value compared to coarser-scale regional climate modelling, particularly in capturing regional climatic features and fine-scale variability. Temperature is reproduced with small biases (mostly within ± 0.5 K), with ICON-CLM performing best, while COSMO-CLM shows a weak cold bias and REMO a warm bias in parts of southern Germany. The CPM models realistically capture daily temperature distributions, though REMO underestimates minimum-temperature extremes. The annual cycle of precipitation is generally well represented, but all CPM models tend to overestimate totals in several regions, e.g., REMO exhibits a distinct spatial bias pattern with stronger deviations along topographic gradients. Extreme precipitation frequencies are generally overestimated, while regional contrasts such as stronger extremes in mountainous regions are preserved. Diurnal cycles show deficiencies specific to the models, including timing errors of afternoon precipitation peaks and misrepresentation of nocturnal precipitation revivals. For global radiation, ICON-CLM achieves the smallest biases, benefiting from its modern radiation scheme (ecRad), whereas COSMO-CLM and REMO show region specific over- and underestimations linked to cloud representation. 10 m wind speed diurnal cycles are best simulated by ICON-CLM, which captures the nocturnal wind minimum, while COSMO-CLM and REMO generally overestimate nighttime wind. Climate indices reveal underestimation of heat-related metrics (summer days and hot days) and systematic overestimation of heavy precipitation indices, although spatial patterns and regional differences are reproduced. In summary, we conclude that NUKLEUS provides valuable climate information for Germany, supporting climate-impact assessments and adaptation planning at municipal to regional scales.

This study presents the evaluation of the historical reference simulations (1961–1990) of the NUKLEUS ensemble, the first kilometer-scale convection-permitting multi-model climate ensemble for Germany. The main goal is to examine to what extent these high-resolution simulations can provide high-quality and actionable information for climate adaptation measures in Germany. The NUKLEUS ensemble comprises nine members, generated by dynamically downscaling three global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) with three regional climate models to a 3 km grid over a Central European domain. The evaluation focuses on the spatio-temporal and statistical representation of basic meteorological variables (temperature, wind speed, and precipitation) and derived application-relevant climate indices compared to reanalyses and observation-based data sets. The analyses are performed for Germany and six pilot regions representing diverse climatic and physiographic settings. The results reveal that the ensemble overall exhibits moderate biases with temperature and precipitation generally showing high distributional skill. Only few simulations exhibit strong warm biases, particularly during summer, while all simulations exhibit a wet bias throughout the year, except local dry biases during summer in individual members. Wind biases are more heterogeneous, particularly due to reference data constraints over complex terrain. Percentile-based climate indices are well reproduced, while fixed-threshold indices show systematic deviations. A comparison with previous regional climate model ensembles highlights the added value of the here-chosen multi-model approach. With regard to downstream applications, a quantile (delta) mapping bias correction is applied to daily precipitation totals and daily mean, minimum, and maximum temperature, which removes climatological biases and markedly improves threshold-based indices. The paper also demonstrates important limitations of this bias correction approach for event-based applications, showing that it may disrupt spatial coherence and introduce spurious spatial gradients in precipitation fields, which can affect the characterization of extreme precipitation events. Overall, the presented analyses support the use of the NUKLEUS ensemble as a high-resolution basis for regional climate and impact studies, while underlining that application-oriented post-processing and validation should be tailored to the target variable and use case.

This study presents the evaluation of NUKLEUS, the first kilometre-scale, multi-model convection-permitting regional climate ensemble (CPM) for Germany. Three state-of-the-art regional climate models (ICON-CLM, COSMO-CLM, and REMO) were run at ~3 km horizontal resolution using a two-step downscaling chain driven by ERA5 reanalysis. The ensemble provides high-resolution climate information for Central Europe. In particular, we evaluate the CPM simulation results for Germany with a focus on six representative pilot regions selected within the German RegIKlim programme. Temperature, precipitation, global radiation, and near-surface wind, including their spatial patterns, annual and diurnal cycles, distributional characteristics, are compared to observational datasets. Moreover, selected climate indices relevant for heat and precipitation extremes are analysed. Overall, the ensemble demonstrates substantial added value compared to coarser-scale regional climate modelling, particularly in capturing regional climatic features and fine-scale variability. Temperature is reproduced with small biases (mostly within ± 0.5 K), with ICON-CLM performing best, while COSMO-CLM shows a weak cold bias and REMO a warm bias in parts of southern Germany. The CPM models realistically capture daily temperature distributions, though REMO underestimates minimum-temperature extremes. The annual cycle of precipitation is generally well represented, but all CPM models tend to overestimate totals in several regions, e.g., REMO exhibits a distinct spatial bias pattern with stronger deviations along topographic gradients. Extreme precipitation frequencies are generally overestimated, while regional contrasts such as stronger extremes in mountainous regions are preserved. Diurnal cycles show deficiencies specific to the models, including timing errors of afternoon precipitation peaks and misrepresentation of nocturnal precipitation revivals. For global radiation, ICON-CLM achieves the smallest biases, benefiting from its modern radiation scheme (ecRad), whereas COSMO-CLM and REMO show region specific over- and underestimations linked to cloud representation. 10 m wind speed diurnal cycles are best simulated by ICON-CLM, which captures the nocturnal wind minimum, while COSMO-CLM and REMO generally overestimate nighttime wind. Climate indices reveal underestimation of heat-related metrics (summer days and hot days) and systematic overestimation of heavy precipitation indices, although spatial patterns and regional differences are reproduced. In summary, we conclude that NUKLEUS provides valuable climate information for Germany, supporting climate-impact assessments and adaptation planning at municipal to regional scales.

This study presents the evaluation of NUKLEUS, the first kilometre-scale, multi-model convection-permitting regional climate ensemble (CPM) for Germany. Three state-of-the-art regional climate models (ICON-CLM, COSMO-CLM, and REMO) were run at ~3 km horizontal resolution using a two-step downscaling chain driven by ERA5 reanalysis. The ensemble provides high-resolution climate information for Central Europe. In particular, we evaluate the CPM simulation results for Germany with a focus on six representative pilot regions selected within the German RegIKlim programme. Temperature, precipitation, global radiation, and near-surface wind, including their spatial patterns, annual and diurnal cycles, distributional characteristics, are compared to observational datasets. Moreover, selected climate indices relevant for heat and precipitation extremes are analysed. Overall, the ensemble demonstrates substantial added value compared to coarser-scale regional climate modelling, particularly in capturing regional climatic features and fine-scale variability. Temperature is reproduced with small biases (mostly within ± 0.5 K), with ICON-CLM performing best, while COSMO-CLM shows a weak cold bias and REMO a warm bias in parts of southern Germany. The CPM models realistically capture daily temperature distributions, though REMO underestimates minimum-temperature extremes. The annual cycle of precipitation is generally well represented, but all CPM models tend to overestimate totals in several regions, e.g., REMO exhibits a distinct spatial bias pattern with stronger deviations along topographic gradients. Extreme precipitation frequencies are generally overestimated, while regional contrasts such as stronger extremes in mountainous regions are preserved. Diurnal cycles show deficiencies specific to the models, including timing errors of afternoon precipitation peaks and misrepresentation of nocturnal precipitation revivals. For global radiation, ICON-CLM achieves the smallest biases, benefiting from its modern radiation scheme (ecRad), whereas COSMO-CLM and REMO show region specific over- and underestimations linked to cloud representation. 10 m wind speed diurnal cycles are best simulated by ICON-CLM, which captures the nocturnal wind minimum, while COSMO-CLM and REMO generally overestimate nighttime wind. Climate indices reveal underestimation of heat-related metrics (summer days and hot days) and systematic overestimation of heavy precipitation indices, although spatial patterns and regional differences are reproduced. In summary, we conclude that NUKLEUS provides valuable climate information for Germany, supporting climate-impact assessments and adaptation planning at municipal to regional scales.

This study presents the evaluation of NUKLEUS, the first kilometre-scale, multi-model convection-permitting regional climate ensemble (CPM) for Germany. Three state-of-the-art regional climate models (ICON-CLM, COSMO-CLM, and REMO) were run at ~3 km horizontal resolution using a two-step downscaling chain driven by ERA5 reanalysis. The ensemble provides high-resolution climate information for Central Europe. In particular, we evaluate the CPM simulation results for Germany with a focus on six representative pilot regions selected within the German RegIKlim programme. Temperature, precipitation, global radiation, and near-surface wind, including their spatial patterns, annual and diurnal cycles, distributional characteristics, are compared to observational datasets. Moreover, selected climate indices relevant for heat and precipitation extremes are analysed. Overall, the ensemble demonstrates substantial added value compared to coarser-scale regional climate modelling, particularly in capturing regional climatic features and fine-scale variability. Temperature is reproduced with small biases (mostly within ± 0.5 K), with ICON-CLM performing best, while COSMO-CLM shows a weak cold bias and REMO a warm bias in parts of southern Germany. The CPM models realistically capture daily temperature distributions, though REMO underestimates minimum-temperature extremes. The annual cycle of precipitation is generally well represented, but all CPM models tend to overestimate totals in several regions, e.g., REMO exhibits a distinct spatial bias pattern with stronger deviations along topographic gradients. Extreme precipitation frequencies are generally overestimated, while regional contrasts such as stronger extremes in mountainous regions are preserved. Diurnal cycles show deficiencies specific to the models, including timing errors of afternoon precipitation peaks and misrepresentation of nocturnal precipitation revivals. For global radiation, ICON-CLM achieves the smallest biases, benefiting from its modern radiation scheme (ecRad), whereas COSMO-CLM and REMO show region specific over- and underestimations linked to cloud representation. 10 m wind speed diurnal cycles are best simulated by ICON-CLM, which captures the nocturnal wind minimum, while COSMO-CLM and REMO generally overestimate nighttime wind. Climate indices reveal underestimation of heat-related metrics (summer days and hot days) and systematic overestimation of heavy precipitation indices, although spatial patterns and regional differences are reproduced. In summary, we conclude that NUKLEUS provides valuable climate information for Germany, supporting climate-impact assessments and adaptation planning at municipal to regional scales.