Enhancing European heatwave characterization: deep learning-based downscaling of global climate data

As part of the OptimESM project, this work aims to prototype a framework for downscaling post-CMIP6 Earth System Models (ESMs) to refine long-term projections up to 2300. This effort focuses on understanding regional climate impacts and extreme events, including heatwaves, droughts, and precipitation extremes, with the goal of supporting robust regional climate projections and informing adaptation strategies across Europe. Within this broader context, our study investigates the application of deep learning techniques to downscale daily temperature fields, enhancing the detection and characterization of European heatwaves through improved spatial resolution. Utilizing the open-source DeepR library based on Transformer architecture, we obtained a five-fold downscaling from ERA5 to CERRA datasets. Performance evaluation highlighted significant improvements in detecting heatwaves, particularly in mountainous areas. Integrating high-resolution orography data increases accuracy by 53%, improving the detection rates of heatwave days from 18% (ERA5) to 27% (DeepR) in regions like southern Norway during the validation period 2015-2020. Despite some perceptual improvement, challenges remain in generalizing across spatial domains and accurately modeling temperature distribution tails, which are critical for extreme events. To address these limitations, we explore advanced architectures such as UNet and Diffusion Models, alongside high-resolution land-cover data and enhanced land-sea masks.