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.

Paula Harder, and co-authors Qidong Yang, Venkatesh Ramesh, Alex Hernandez-Garcia, Prasanna Sattigeri, Campbell D. Watson, Daniela Szwarcman and David Rolnick, recently wrote a paper on Generating physically-consistent high-resolution climate data with hard-constrained neural networks. In this interview, Paula tells us more about how they developed a method for super-resolution climate data where conservation laws are enforced. Our paper looks at super-resolution for climate data, which is called downscaling. Deep learning has been applied a lot recently in that area, but the neural networks employed tend to violate physical laws, such as mass conservation. In this work, we look at how to change neural super-resolution architectures such that given constraints like conservation laws are enforced.