A variable-resolution unstructured spatio-temporal graph neural network: an application to very short-range weather forecasting in Guangdong, China

Abstract The increase in extreme weather events has raised the demand for very short-range forecasts in Guangdong, the most populous province in China. Although global AI-based weather forecasting models provide strong medium-range guidance, they remain suboptimal for regional very short-range predictions. To address this, we developed a spatio-temporal graph neural network (STGNN) for Guangdong, featuring an unstructured, variable-resolution graph refined over the Pearl River Delta. The core of