  Accurate precipitation forecasting is a vital challenge of both scientific
and societal importance. Data-driven approaches have emerged as a widely used
solution for addressing this challenge. However, solely relying on data-driven
approaches has limitations in modeling the underlying physics, making accurate
predictions difficult. Coupling AI-based post-processing techniques with
traditional Numerical Weather Prediction (NWP) methods offers a more effective
solution for improving forecasting accuracy. Despite previous post-processing
efforts, accurately predicting heavy rainfall remains challenging due to the
imbalanced precipitation data across locations and complex relationships
between multiple meteorological variables. To address these limitations, we
introduce the PostRainBench, a comprehensive multi-variable NWP post-processing
benchmark consisting of three datasets for NWP post-processing-based
precipitation forecasting. We propose CAMT, a simple yet effective Channel
Attention Enhanced Multi-task Learning framework with a specially designed
weighted loss function. Its flexible design allows for easy plug-and-play
integration with various backbones. Extensive experimental results on the
proposed benchmark show that our method outperforms state-of-the-art methods by
6.3%, 4.7%, and 26.8% in rain CSI on the three datasets respectively. Most
notably, our model is the first deep learning-based method to outperform
traditional Numerical Weather Prediction (NWP) approaches in extreme
precipitation conditions. It shows improvements of 15.6%, 17.4%, and 31.8% over
NWP predictions in heavy rain CSI on respective datasets. These results
highlight the potential impact of our model in reducing the severe consequences
of extreme weather events.
