Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network

Abstract Precipitation exerts far‐reaching impacts on both socio‐economic fabric and individual well‐being, necessitating concerted efforts in accurate forecasting. Deep learning (DL) models have increasingly demonstrated their prowess in forecasting meteorological elements. However, traditional DL prediction models often grapple with heavy rainfall forecasting. In this study, we propose physics‐informed localized graph neural network models called ω‐GNN and ω‐EGNN, constrained by the coupling of physical variables and climatological background to predict precipitation in China. These models exhibit notable and robust improvements in identifying heavy rainfall while maintaining excellent performance in forecasting light rain by comparing to numerical weather prediction (NWP) and other DL models with multiple perturbation experiments in different data sets. Surprisingly, within a certain range, even when a DL model utilizes more input variables, GNN can still maintain its advantage. The methods to fuse physics into DL model demonstrated in this study may be promising and call for future studies.