Scale-Aware Machine Learning for Precipitation Downscaling: Impact on Regional Applications in Europe

Accurate high-resolution precipitation is critical for hydrological modelling, climate impact assessment, and flood risk analysis, yet reanalysis products like ERA5 often lack the necessary spatial detail required at regional scales. This study investigates machine learning-based super-resolution techniques for precipitation downscaling, specifically examining scale-dependency and uncertainty.

We test several downscaling strategies, including convolutional neural networks with channel‑attention mechanisms and generative diffusion models. Precipitation fields are downscaled from coarse-resolution ERA5 inputs (0.25° resolution) to finer spatial resolutions using gridded observational datasets as reference: E‑OBS (0.125°) for pan‑European evaluation and, for selected regions, higher‑resolution products such as EMO‑1 (~1 km). By considering multiple scale factors, we adopt a scale‑aware framework that quantifies how downscaling skill and the associated uncertainty in super-resolution machine learning methods vary with spatial resolution and with the choice of reference dataset.

Model evaluation combines conventional accuracy metrics with diagnostics of field structure, focusing on spatial heterogeneity, intensity‑dependent behaviour (including extremes), and robustness across seasons and climatic regimes. We also discuss how scale‑dependent changes in precipitation variability and spatial structure can inform uncertainty characterisation for machine‑learning downscaling and guide its use in regional hydrological modelling and flood‑risk assessments across Europe.