With an increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method, using generative adversarial networks (GANs), for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). We show that by training a GAN model with ERA5 low-resolution input and Wind Integration National Dataset Toolkit (WTK; [[1]](#ref-0001)) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. GANs are trained on data sampled from the contiguous United States (CONUS), selected to provide a diverse sampling of terrain conditions, and validated on data held out from training, as well as observational data. This cross-validation shows low error and high correlations with observations and excellent agreement with holdout data across distributions of physical metrics. We additionally downscaled the members of the European Centre for Medium-Range Weather Forecasting Ensemble of Data Assimilations (EDA) for 2012–2015 and 2019–2023 to estimate uncertainty over the period for which we have observational data. We applied this approach to downscale 30-km hourly ERA5 data to 2-km 5-minute wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. The geographic extent was motivated by the urgent need for planners in Ukraine to rebuild and decentralize the grid, which has been severely damaged by the conflict between Russia and Ukraine. Comparisons against observational data from the Meteorological Assimilation Data Ingest System (MADIS) and multiple wind farms show comparable performance to the CONUS validation. This 24-year data record is the first member of the super resolution for renewable energy resource data with wind from reanalysis data dataset (Sup3rWind).