Bias-targeted deep learning enhances short-range heavy rainfall forecasts

Improving short-range forecasts of heavy rainfall remains a challenge. In recent years, deep-learning-based methods have been increasingly explored for post-processing precipitation forecasts from numerical weather prediction (NWP) models, but their performances are constrained by the non-negative and heavy-tailed nature of rainfall. Mainstream studies tried to solve this problem by redesigning loss functions or constructing hybrid models, yet they struggled to achieve both simplicity and transferability. Here, we show that the biases between NWP forecasts and observations in heavy rainfall events follow an approximately Gaussian distribution. Accordingly, this study trains a multi-task U-Net that uses precipitation biases as the target. This bias-targeted strategy produces stable and substantial enhancements in short-range heavy rainfall forecasts, with threat score improvements exceeding 21% across four in five regions of China. The findings highlight the critical role of target selection in deep-learning-based post-processing and provide a simple and effective pathway for advancing heavy rainfall forecasts.