Generative Diffusion Downscaling for the Alps: Benchmarking CorrDiff against MeteoSwiss Operational NWP Ensemble

Kilometre-scale weather and climate datasets are invaluable for quantifying, forecasting and projecting hazards in areas of complex topography, such as the Alps. However, producing such datasets using traditional numerical weather prediction (NWP) models is becoming prohibitively expensive, particularly for climate-timescale simulations and large ensembles. Probabilistic generative downscaling offers a potential alternative, as it learns the conditional mapping from coarse global drivers to kilometre-scale regional fields.

Here, we evaluate a modified conditional generative correction–diffusion model (CorrDiff) for downscaling the ERA5 and IFS-ENS datasets over the Greater Alpine Region. The modified CorrDiff model was trained using a 20-year, 1-km resolution dataset produced with the ICON numerical model, with precipitation constrained to Swiss radar observations using a latent-heat nudging scheme. This setup allows us to make a direct comparison with MeteoSwiss' operational NWP ensemble.

Verification against observations and gridded products reveals that CorrDiff achieves competitive performance following substantial targeted adaptations to the model. Although not explicitly encoded in the loss function, the adapted model reproduces emergent climatological indices, including the diurnal cycle of land precipitation and exceedance probabilities for heavy precipitation. It also captures the spatial patterns of consecutive dry and wet days, as well as prevailing wind direction and directional variability.