Deep Learning for Downscaling Tropical Cyclone Rainfall to Hazard‐Relevant Spatial Scales

Abstract Flooding, driven in part by intense rainfall, is the leading cause of mortality and damages from the most intense tropical cyclones (TCs). With rainfall from TCs set to increase under anthropogenic climate change, it is critical to accurately estimate extreme rainfall to better support short‐term and long‐term resilience efforts. While high‐resolution climate models capture TC statistics better than low‐resolution models, they are computationally expensive. This leads to a trade‐off between capturing TC features accurately, and generating large enough simulation data sets to sufficiently sample high‐impact, low‐probability events. Downscaling can assist by predicting high‐resolution features from relatively cheap, low‐resolution models. Here, we develop and evaluate a set of three deep learning models for downscaling TC rainfall to hazard‐relevant spatial scales. We use rainfall from the Multi‐Source Weighted‐Ensemble Precipitation observational product at a coarsened resolution of ∼100 km, and apply our downscaling model to reproduce the original resolution of ∼10 km. We find that the Wasserstein Generative Adversarial Network is able to capture realistic spatial structures and power spectra and performs the best overall, with mean biases within 5% of observations. We also show that the model can perform well at extrapolating to the most extreme storms, which were not used in training.