A Probabilistic U-Net Approach to Downscaling Climate Simulations

Maryam Alipourhajiagha, Pierre-Louis Lemaire, Youssef Diouane, Julie Carreau

Additional Submission Instructions: For the camera-ready version, please include the author names and affiliations, funding disclosures, and acknowledgements.

Track: Track 1: Original Research/Position/Education/Attention Track

Keywords: Probabilistic U-Net, Statistical Downscaling, Precipitation Extremes, Uncertainty Quantification, Generative Deep Learning

Abstract: Climate models are limited by heavy computational costs, often producing outputs at coarse spatial resolutions, while many climate change impact studies require finer scales. Statistical downscaling bridges this gap, and we adapt the probabilistic U-Net for this task, combining a deterministic U-Net backbone with a variational latent space to capture aleatoric uncertainty. We evaluate four training objectives, afCRPS and WMSE–MS-SSIM with three settings for downscaling precipitation and temperature from $16\times$ coarser resolution. Our main finding is that WMSE–MS-SSIM performs well for extremes under certain settings, whereas afCRPS better captures spatial variability across scales.

Submission Number: 415