Correcting dry/wet classification bias in precipitation downscaling via generative adversarial networks

Abstract Accurately downscaling precipitation from coarse to high spatial resolutions remains a critical challenge in climate and hydrometeorological modeling. A key limitation is the frequent misclassification of dry and wet regions, which compromises the realism and reliability of high-resolution outputs. To address this, we propose a deep learning-based downscaling framework that explicitly models dry/wet classification by transforming low-resolution 6×6 precipitation inputs into high-resolution 60 × 60 binary classification fields. We evaluate two architectures, a convolutional encoder-decoder and a conditional Wasserstein generative adversarial network (WGAN), utilizing three training strategies: (1) using binary wet/dry inputs, (2) using precipitation intensity inputs, and (3) using precipitation intensity inputs and adding physical constraints. Models are trained and validated on both synthetically generated precipitation fields and real radar-estimated hourly precipitation data over the contiguous United States. Performance is assessed using metrics including the overall probability of zero (