Regional climate simulations with the MAR V 3.9 model by University of Liège. Dynamical downscaling on the CORDEX EUR-11 domain and HRes domain over Belgium at convection-permitting scale. Model name: MAR V. 3.9 Important reference: Wyard et al. 2017: https://dx.doi.org/10.1002/joc.4879 Resolution: RCM: 50 km and 12.5 km; LAM: 5 km Nr. vertical levels: 30 Time step (s): Important scheme: Snow variables Focal time series / severity index: Snowfall events and snowmelt events inducing floods Host GCM: ERA-Interim / various Non-hydrostatic: no

Regional climate simulations with the ALARO-0 model by the Royal Meteorological Institute of Belgium and Ghent University (RMIB-UGent). Dynamical downscaling on the CORDEX EUR-11 domain and HRes domain over Belgium at convection-permitting scale. Model name: ALARO-0 Important reference: Giot et al. 2016: https://dx.doi.org/10.5194/gmd-9-1143-2016 Resolution: RCM: 50 km and 12.5 km; LAM: 4 km Nr. vertical levels: 46 Time step (s): 900 (50 km); 300 (12.5 km); 180 (4 km) Important scheme: 3MT Focal time series / severity index: (Sub-)hourly precipitation Host GCM: ERA-Interim / ARPEGE Non-hydrostatic: no

This is an atmospheric hourly hindcast for the German Bight using COSMO-CLM version 5.00_clm2 from 1948-August 2015 (http://www.cosmo-model.org/content/model/documentation/core/default.htm). The model uses a rotated grid with 250 x 180 grid points and a grid point distance of 0.025 degrees, the rotated North pole is located at 172.97 W, 34.925 N. The forcing is coastDat2 doi:10.1594/WDCC/coastDat-2_COSMO-CLM . In rotated coordinates the model area extends from 2.25 W to 2.25 E, 3.125 S to 3.125 N, in geographical coordinates this corresponds to about 1.3 E to 12.8 E, 52.7 N to 57.3 N.

Regional climate simulations with the ALARO-0 model by the Royal Meteorological Institute of Belgium and Ghent University (RMIB-UGent). Dynamical downscaling on the CORDEX EUR-11 domain and HRes domain over Belgium at convection-permitting scale. Model name: ALARO-0 Important reference: Giot et al. 2016: https://dx.doi.org/10.5194/gmd-9-1143-2016 Resolution: RCM: 50 km and 12.5 km; LAM: 4 km Nr. vertical levels: 46 Time step (s): 900 (50 km); 300 (12.5 km); 180 (4 km) Important scheme: 3MT Focal time series / severity index: (Sub-)hourly precipitation Host GCM: ERA-Interim / ARPEGE Non-hydrostatic: no

Regional climate simulations with the COSMO-CLM V. 6.0-CLM6 model by UCLouvain. Dynamical downscaling on the CORDEX EUR-11 domain and HRes domain over Belgium at convection-permitting scale. Model name: COSMO-CLM V. 6.0-CLM6 Important reference: Wyard et al. 2017: https://dx.doi.org/10.1002/joc.4879 Resolution: RCM: 12.5km; LAM: 2.8 km Nr. vertical levels: 40 Time step (s): 80 (12.5 km); 20 (2.8 km) Important scheme: Two-moment microphysics scheme Focal time series / severity index: Hail mixing ratio and number concentration, detailed precipitation Host GCM: ERA-Interim / MPI-ESM Non-hydrostatic: yes

Regional climate simulations with the MAR V 3.9 model by University of Liège. Dynamical downscaling on the CORDEX EUR-11 domain and HRes domain over Belgium at convection-permitting scale. Model name: MAR V. 3.9 Important reference: Wyard et al. 2017: https://dx.doi.org/10.1002/joc.4879 Resolution: RCM: 50 km and 12.5 km; LAM: 5 km Nr. vertical levels: 30 Time step (s): Important scheme: Snow variables Focal time series / severity index: Snowfall events and snowmelt events inducing floods Host GCM: ERA-Interim / various Non-hydrostatic: no

This database offers highly valuable climate information for the Sierra Nevada (SN) mountain range, identified as a double climate-change hotspot since it constitutes a semi-arid mountain system within the Mediterranean, a region especially vulnerable to climate change. Moreover, SN is an area where high-quality climate data are particularly scarce, largely due to its difficult accessibility. Present climate data (1991–2022) at very high spatial resolution (1 km) for Sierra Nevada (SN), the highest mountain range in the Iberian Peninsula (IP), located in southeastern Andalusia (Spain). The data were generated using version 4.3.3 of the Weather Research and Forecasting (WRF) model (Skamarock et al., 2021), driven by ERA5 reanalysis data (Hersbach et al., 2018). The planetary boundary layer (PBL) scheme used was the Asymmetric Convective Model version 2 (ACM2; Pleim, 2007). Both longwave and shortwave radiation were parameterized using the Community Atmosphere Model version 3.0 (CAM3.0; Collins et al., 2004). The microphysics scheme applied was the WRF Single-Moment 7-class scheme (WSM7; Bae et al., 2019), and the land surface model used was NOAH-MP (Niu et al., 2011). Convection was explicitly resolved (i.e., no cumulus parameterization was used). The dataset is organized into four categories: Primary Climate Variables (72 files): Daily values of relative humidity, net radiation, accumulated precipitation, surface pressure, maximum temperature, minimum temperature, mean temperature, and wind speed; and hourly values of accumulated precipitation and mean temperature for the entire period. Hourly Precipitation Extremes (3 files): Frequency and intensity (Fwet and Iwet respectively) of wet hours (precipitation > 0.1 mm/hour) and the maximum hourly precipitation during the wettest month. ETCCDI Extreme Indices (12 files): Annual values of selected indices from the Expert Team on Climate Change Detection and Indices (ETCCDI), including: Consecutive Dry Days (CDD), Daily Temperature Range (DTR), Growing Season Length (GSL), Icing Days (ID), Number of Wet Days (R1mm), Heavy Precipitation Days (R10mm), Very Heavy Precipitation Days (R20mm), Wettest Pentad (Rx5day), Simple Daily Intensity Index (SDII), Frost Days (TNltm2), Coldest Night (TNn), and Warmest Day (TXx). Bioclimatic Variables (18 files): Annual and seasonal mean temperature (BIO1 and BIO1*), annual mean maximum temperature (BIOmax and BIO1max*), annual mean minimum temperature (BIOmin and BIO1min*), isothermality (BIO3), temperature seasonality (BIO4), maximum temperature of the warmest month (BIO5), minimum temperature of the coldest month (BIO6), annual temperature range (BIO7), mean temperature of the wettest quarter (BIO8), mean temperature of the driest quarter (BIO9), annual precipitation (BIO12), seasonal mean precipitation (BIO12*), precipitation of the wettest month (BIO13), precipitation seasonality (BIO15), and precipitation of the coldest quarter (BIO19). For more detailed information on the variables, see García-Valdecasas Ojeda et al. (2025). This version has data updated to 2022 in the evaluation period and, as a new feature, adds hourly resolution climate information on temperature and precipitation, as well as new derived variables such as various ETCCDI indices or climate variables that are essential for characterizing the mountain climate in this region.

This database offers highly valuable climate information for the Sierra Nevada (SN) mountain range, identified as a double climate-change hotspot since it constitutes a semi-arid mountain system within the Mediterranean, a region especially vulnerable to climate change. Moreover, SN is an area where high-quality climate data are particularly scarce, largely due to its difficult accessibility. Pseudo-projected climate data (1991-2020 + climate change signal of a set of 24 CMIP6 GCMs for the period 2070-2099 under the SSP5-8.5) at very-high spatial resolution (1 km) for Sierra Nevada, the highest mountain region in the Iberian Peninsula (IP) located in southeastern Andalusia (Spain). Data obtained using the Weather Research & Forecasting (WRF) model v4.3.3 (Skamarock et al., 2021) driven by the ERA5 reanalysis (Hersbach et al., 2018) + climate change signal of a set of 24 CMIP6 GCMs for the period 2070-2099 under the SSP5-8.5. The planetary boundary layer (PBL) scheme used was the Asymmetric Convective Model version 2 (ACM2; Pleim, 2007). Both longwave and shortwave radiation were parameterized using the Community Atmosphere Model version 3.0 (CAM3.0; Collins et al., 2004). The microphysics scheme applied was the WRF Single-Moment 7-class scheme (WSM7; Bae et al., 2019), and the land surface model used was NOAH-MP (Niu et al., 2011). Convection was explicitly resolved (i.e., no cumulus parameterization was used). The dataset is organized into four categories: Primary Climate Variables (68 files): Daily values of relative humidity, net radiation, accumulated precipitation, surface pressure, maximum temperature, minimum temperature, mean temperature, and wind speed; and hourly values of accumulated precipitation and mean temperature for the entire period. Hourly Precipitation Extremes (3 files): Frequency and intensity (Fwet and Iwet respectively) of wet hours (precipitation > 0.1 mm/hour) and the maximum hourly precipitation during the wettest month. ETCCDI Extreme Indices (12 files): Annual values of selected indices from the Expert Team on Climate Change Detection and Indices (ETCCDI), including: Consecutive Dry Days (CDD), Daily Temperature Range (DTR), Growing Season Length (GSL), Icing Days (ID), Number of Wet Days (R1mm), Heavy Precipitation Days (R10mm), Very Heavy Precipitation Days (R20mm), Wettest Pentad (Rx5day), Simple Daily Intensity Index (SDII), Frost Days (TNltm2), Coldest Night (TNn), and Warmest Day (TXx). Bioclimatic Variables (18 files): Annual and seasonal mean temperature (BIO1 and BIO1*), annual mean maximum temperature (BIOmax and BIO1max*), annual mean minimum temperature (BIOmin and BIO1min*), isothermality (BIO3), temperature seasonality (BIO4), maximum temperature of the warmest month (BIO5), minimum temperature of the coldest month (BIO6), annual temperature range (BIO7), mean temperature of the wettest quarter (BIO8), mean temperature of the driest quarter (BIO9), annual precipitation (BIO12), seasonal mean precipitation (BIO12*), precipitation of the wettest month (BIO13), precipitation seasonality (BIO15), and precipitation of the coldest quarter (BIO19). This version has data updated to 2022 in the evaluation period and, as a new feature, adds hourly resolution climate information on temperature and precipitation, as well as new derived variables such as various ETCCDI indices or climate variables that are essential for characterizing the mountain climate in this region.

This is an atmospheric hourly hindcast for the German Bight using COSMO-CLM version 5.00_clm2 from 1948-August 2015 (http://www.cosmo-model.org/content/model/documentation/core/default.htm). The model uses a rotated grid with 250 x 180 grid points and a grid point distance of 0.025 degrees, the rotated North pole is located at 172.97 W, 34.925 N. The forcing is coastDat2 doi:10.1594/WDCC/coastDat-2_COSMO-CLM . In rotated coordinates the model area extends from 2.25 W to 2.25 E, 3.125 S to 3.125 N, in geographical coordinates this corresponds to about 1.3 E to 12.8 E, 52.7 N to 57.3 N.

The experiment aims to investigate how the representation of convection influences the West African Monsoon during the mid-Holocene. Atmospheric and SST input data originate from the MPI-ESM Holocene simulations reflecting Holocene condition. External Parameters (surface condition) reflect present-day conditions similar to the experimental setup of PMIP1: The Sahara remains a desert. We use the ICON (ICOsahedral Nonhydrostatic) model framework version 2.5.0 (see Zängl et al. (2014) for more details). The provided data covers one simulation from June to October (JJASO) for the year 7023 before present (BP) with the year 2000 as the reference year. The time axes of the NetCDF files reflect the model year which is based on the time axes of the MPI-ESM slo0021a Holocene simulations. The artificial model year 1001 in slo0021a refers to the year 8000 BP. Therefore, the model year 1977 refers to the year 7023 BP. The experiment compares a 5km horizontal resolution, cloud-resolving simulation with a 40km-horizontal resolution, parameterized convection simulation. The 40km-domain (DOM01) covers a range from 70.5°W - 99.5°E; 49°S - 59°N The 5km-domain (DOM04) covers a range from 37°W - 53°E; 0°N - 40°N The dataset provides daily mean values on the triangular ICON grid. The datasets provide atmospheric (3D), surface (2D) and precipitation (2D) data an the following variables: rain_con_rate, rain_gsp_rate, clct, geopot, temp, rh, qv, u, v, w, w_so, runoff_g, runoff_s, lhfl_s, shfl_s, soiltyp