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价值与社会意义的重要挑战。数据驱动方法已成为解决该问题的常用方案，但仅依赖数据驱动方法在模拟底层物理过程方面存在局限性，导致难以实现精准预测。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，为提高预报精度提供了更有效的解决方案。尽管已有后处理技术的研究，但降水数据在不同位置的不平衡分布以及多个气象变量间的复杂关系，使得准确预报强降雨仍面临挑战。针对这些局限，我们提出了PostRainBench——一个基于NWP后处理的降水预报综合多变量基准，包含三个数据集。我们提出了CAMT（通道注意力增强多任务学习框架），该框架采用简洁有效的加权损失函数设计，其灵活架构支持与多种主干网络便捷集成。在所提基准上的大量实验表明，该方法在三个数据集上的雨CSI指标分别以6.3%、4.7%和26.8%的优势超越现有最优方法。特别值得关注的是，本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，在各自数据集的重雨CSI上较NWP预测分别提升15.6%、17.4%和31.8%。这些结果凸显了本模型在减轻极端天气事件严重后果方面的潜在影响。