Benchmarking the geographic generalization of deep learning models for precipitation downscaling.

ABSTRACT: Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolution for local-scale risk-assessments is not computationally feasible. Deep learning-based super-resolution models offer a promising solution to downscale ESM outputs to higher resolutions by learning from data. Yet, due to regional variations in climatic processes, these models typically require retraining for each geographical area-demanding high-resolution observational data, which is unevenly available across the globe. This highlights the need to assess how well these models generalize across geographic regions. To address this, we introduce RainShift, a dataset and benchmark for evaluating downscaling under geographic distribution shifts. We eval

SUBMITTER: Harder P 

PROVIDER: S-EPMC12852169 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature