An AI-based downscaling tool to generate a large ensemble of high-resolution wind storm footprints over Europe

Midlatitude winter storms are a major cause of economic loss and infrastructure damage across Europe. Although reanalysis datasets, such as ERA5, offer reliable near-surface wind gust fields from 1940 onwards, the limited set of winter storm events remains inadequate for catastrophe models. The LArge Ensemble of Regional climaTe modEl Simulations for EUrope (LAERTES-EU) dataset addresses this limitation by providing over 12,000 years of synthetic climate data, yielding a substantially larger catalogue of possible winter storm events. However, a closer analysis revealed that its coarse spatial resolution (~27 km) systematically underestimates extreme wind gusts, which are critical for catastrophe models. High-resolution regional climate model (RCM) simulations using the Icosahedral Nonhydrostatic (ICON) model at 2.5 km grid spacing can accurately capture these extremes. As dynamical downscaling of the entire LAERTES-EU dataset is computationally extortionate, other solutions are required. This study presents a deep learning-based approach, commonly known as Super Resolution (SR), as a cost-effective alternative. Specifically, a probabilistic Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) was trained using pairs of coarse-resolution (ERA5, ~25 km) and high-resolution (ICON, ~2.5 km) data in order to downscale wind gust fields from approximately 300 historical winter storms. Our results show that the WGAN-GP model generates high-resolution wind gust fields that are statistically similar to the ICON simulations, but with much lower computational costs. The trained model is then employed to downscale wind gust fields of winter storm events from the LAERTES-EU ensemble, producing a large dataset of high-resolution synthetic storm events suitable for detailed risk assessment and climate impact studies.