{"ID":2834383,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01400","arxiv_id":"2512.01400","title":"On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling","abstract":"Deep learning offers promising capabilities for the statistical downscaling of climate and weather forecasts, with generative approaches showing particular success in capturing fine-scale precipitation patterns. However, most existing models are region-specific, and their ability to generalize to unseen geographic areas remains largely unexplored. In this study, we evaluate the generalization performance of generative downscaling models across diverse regions. Using a global framework, we employ ERA5 reanalysis data as predictors and IMERG precipitation estimates at $0.1^\\circ$ resolution as targets. A hierarchical location-based data split enables a systematic assessment of model performance across 15 regions around the world.","short_abstract":"Deep learning offers promising capabilities for the statistical downscaling of climate and weather forecasts, with generative approaches showing particular success in capturing fine-scale precipitation patterns. However, most existing models are region-specific, and their ability to generalize to unseen geographic area...","url_abs":"https://arxiv.org/abs/2512.01400","url_pdf":"https://arxiv.org/pdf/2512.01400v1","authors":"[\"Paula Harder\",\"Christian Lessig\",\"Matthew Chantry\",\"Francis Pelletier\",\"David Rolnick\"]","published":"2025-12-01T08:24:40Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}