A precipitation downscaling method using a super-resolution deconvolution neural network with step orography

Abstract Coarse spatial resolution in gridded precipitation datasets, reanalysis, and climate model outputs restricts their ability to characterize the localized extreme rain events and limits the use of the coarse resolution information for local to regional scale climate management strategies. Deep learning models have recently been developed to rapidly downscale the coarse resolution precipitation to the high local scale resolution at a much lower cost than dynamic downscaling. However, these existing super-resolution deep learning modeling studies have not rigorously evaluated the model’s skill in producing fine-scale spatial variability, particularly over topographic features. These current deep-learning models also have difficulty predicting the complex spatial structure of extreme events. Here, we develop a model based on super-resolution deconvolution neural network (SRDN) to downscale the hourly precipitation and evaluate the predictions. We apply three versions of the SRDN model: (a) SRDN (no orography), (b) SRDN-O (orography only at final resolution enhancement), and (c) SRDN-SO (orography at each step of resolution enhancement). We assess the ability of SRDN-based mo