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.

翻译：准确的降水预报是兼具科学意义与社会重要性的关键挑战。数据驱动方法已成为应对这一挑战的广泛采用方案。然而，仅依赖数据驱动方法在建模潜在物理过程方面存在局限性，导致精确预报困难。将基于人工智能的后处理技术与传统数值天气预报（NWP）方法相结合，为提升预报精度提供了更有效的解决方案。尽管已有后处理研究，但降水量数据在不同位置存在不平衡性，且多个气象变量间关系复杂，因此准确预报强降雨仍具挑战性。为解决这些局限，我们提出PostRainBench——一个基于NWP后处理降水预报的综合多变量基准，包含三个数据集。我们提出CAMT——一种简单高效、采用通道注意力增强的多任务学习框架，并配有特殊设计的加权损失函数。其灵活设计支持与多种主干网络进行便捷的即插即用集成。在提出的基准上进行的广泛实验结果表明，该方法在三个数据集上的雨量临界成功指数（CSI）分别比现有最优方法高出6.3%、4.7%和26.8%。尤为重要的是，我们的模型是首个在极端降水条件下超越传统数值天气预报（NWP）方法的深度学习方法。在对应数据集的强降雨CSI指标上，该方法较NWP预报分别提升15.6%、17.4%和31.8%。这些结果突显了该模型在减轻极端天气事件严重后果方面的潜在影响力。