On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling

• URL: http://arxiv.org/abs/2512.01400v1
• Date: Mon, 01 Dec 2025 08:24:40 GMT
• Title: On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling
• Authors: Paula Harder, Christian Lessig, Matthew Chantry, Francis Pelletier, David Rolnick,
• Abstract summary: We evaluate the generalization performance of generative downscaling models across diverse regions.<n>A hierarchical location-based data split enables a systematic assessment of model performance across 15 regions around the world.
• Score: 14.69597733830489
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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