The wind dataset was produced using the Weather Research and Forecasting (WRF) model, version 4.0.2. The model spatial configuration comprises one outer coarse-resolution domain at 9 km (d01) and two nested domains at 3 km (d02 and d03) horizontal spatial gridding. The domains d02 and d03 cover the Galapagos Islands and Ecuador’s mainland, respectively. The physics parametrization schemes follow the model configuration used for the production of the New European Wind Atlas (NEWA). The initial and boundary conditions for the WRF simulations were obtained from ERA5 reanalysis data. The simulations were performed month by month for the 14-year period. Each month was split into four runs and a 24-hour spin-up period was added at the beginning of each run. The post-processing of the WRF simulations follows the procedure described in Dörenkämper et al. (2020). The data provided here comprises hourly values from 00:00 UTC 01-01-2005 to 23:00 UTC 31-12-2018 of wind speed, wind direction and wind power density at seven vertical levels (30, 40, 50, 60, 70, 80, and 100 m above ground level). Note: The NetCDF files of this dataset were packed to reduce the data volume. To unpack the packed variables, please use the operator "unpack" in CDO. Acknowledgements: Thanks to Jonathan Chu, Martin Dörenkämper, and the NEWA team for their assistance on the configuration of the WRF model. Thanks to the High-Performance Computing Team from the University of Oldenburg for their computing facilities.

Copernicus Urban Atlas data were complemented by Corine Landcover data to generate an area covering land cover dataset for DWD’s urban climate model MUKLIMO_3. The land cover classifaction of the combined data set is identical to the original classification of the two input data sets and documented on the websites of the Copernicus Land Monitoring Service (https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012, https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012). The dataset was created within the Copernicus project ‘Utilisation of GMES Urban Atlas for urban climate modelling’ (GUAMO). The project was funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI) of Germany under reference number ‘50 EW 1610‘ and carried out from August 2016 to September 2018. The dataset a polygon-feature (feature-class) of an ESRI Geodatabase with following coordinate system: ETRS_1989_LAEA WKID: Authority: 3035 (EPSG) Projection: Lambert_Azimuthal_Equal_Area False_Easting: 4321000,0 False_Northing: 3210000,0 Central_Meridian: 10,0 Latitude_Of_Origin: 52,0 Linear Unit: Meter (1,0) Geographic Coordinate System: GCS_ETRS_1989 Angular Unit: Degree (0,0174532925199433) Prime Meridian: Greenwich (0,0) Datum: D_ETRS_1989 Spheroid: GRS_1980 Semimajor Axis: 6378137,0 Semiminor Axis: 6356752,314140356 Inverse Flattening: 298,257222101

Graphs on validation of mean cloud fractional cover product, provided by ECSM - European Climate System Monitoring, WMO Regional Climate Centre (RCC) on Climate Monitoring

The climatological dataset was produced using the Weather and Research Forecasting (WRF) model, version 4.2.2, configured with two nested domains at 10 km (D1) and 2 km (D2) horizontal grid spacing. It covers most of the South Island of New Zealand and is centered over Brewster Glacier in the Southern Alps. The model was forced every three hours by ERA5 reanalysis data at its outer lateral boundaries. The dataset spans the period of 1 January 2005 to 31 December 2020, providing daily output in the outer domain (D1) and 3-hourly output in the innermost domain (D2). The data provided here are a selection of daily averages from the inner WRF domain (D2; 2-km grid spacing). They are distributed among three different file types containing 4-dimensional, 3-dimensional and time-invariant output variables, respectively. For the 4-dimensional fields, perturbation and base-state atmospheric pressure (WRF variables P and PB) and geopotential (PH and PHB) were combined to produce full model fields (PRES and GEOPT). Perturbation potential temperature (T) was converted to total potential temperature (THETA). Wind vectors (U,V, and W) were converted to mass points and rotated to earth coordinates. ------- Acknowledgements: The modeling and related research was supported by the German Research Foundation (DFG) grant no. 453305163. The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b128dc / ATMOS ("Numerical atmospheric modeling for the attribution of climate change and for model improvement"). NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG) – 440719683.

Description of climatological monthly and annual outstanding weather events, maps of various climate quantities, provided by WMO RA VI Regional Climate Centre (RCC) an Climate Monitoring WMO-RA6-RCC-CM

Maps of monthly mean snow depth derived from SYNOP observations on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring WMO-RA6-RCC-CM

Maps of monthly normals 1981-2007 of number of snowdays derived from SYNOP observations on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring WMO-RA6-RCC-CM

Maps of longterm monthly means (1980-2005) of precipitable water derived from SATEM bulletins by gridding to 5x5 degree grid and interpolation to a 1x1 degree grid, provided by WMO Regional Climate Centre (RCC) on Climate Monitoring

Maps of monthly maximum duration of fair days derived from from satellite and in-situ observations ('satellite weather') on a 0.25x0.25 degree grid (near real time product), provided by WMO Regional Climate Centre (RCC) on Climate Monitoring

Maps of monthly normals 1981-2007 of mean snow depth derived from SYNOP observations on a 0.1x0.1 degree grid, provided by WMO RA VI Regional Climate Centre (RCC) on Climate Monitoring WMO-RA6-RCC-CM