Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning

Anamitra Saha, Sai Ravela

Modeling the risk of extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Although the available low-resolution climate models capture different scenarios, accurate risk assessment for mitigation and adaption often demands details that they typically cannot resolve. Here, we develop a dynamic data-driven downscaling (super-resolution) method that incorporates physics and statistics in a generative framework to learn the fine-scale spatial details of rainfall. Our method transforms coarse-resolution (0.25∘×0.25∘) climate model outputs into high-resolution (0.01∘×0.01∘) rainfall fields while efficaciously quantifying uncertainty. Results indicate that the downscaled rainfall fields closely match observed spatial fields and their risk distributions.

Saha, A., & Ravela, S. (2022). Downscaling Extreme Rainfall Using Physical-Statistical Generative Adversarial Learning. arXiv preprint arXiv:2212.01446.