Generative Hierarchical Mesh Network for Global Station Weather Probabilistic Forecasting

Global Station Weather Forecasting (GSWF) is pivotal for energy, aviation, and agriculture, yet existing time series methods struggle with its core challenges: capturing hierarchical dependencies and inherent uncertainty of chaotic meteorological systems, while adapting to global weather station data’s uneven spatial distribution and limited size. To address these, we propose a novel Generative Hierarchical Mesh Network (GHMN). Specifically, we construct a hierarchical graph with bidirectional cross-level mapping, encoding bottom-layer local details into top-layer global features, and propagate global context downward for fine-grained predictions. For uncertainty modeling, we design a lightweight flow matching-based generative head that takes deterministic outputs as conditions to generate plausible ensemble predictions without preset priors. By decoupling forecasting into deterministic mean prediction and residual uncertainty estimation, we reduce learning complexity. Experiments show GHMN outperforms spatiotemporal baselines by up to 11%, with the advantage of probabilistic prediction.