## 2 Related Work
### Spatiotemporal Meteorological Forecasting  
Spatiotemporal meteorological forecasting is crucial in disaster warning, environmental management, and sustainable development, and its accuracy is directly related to public safety and energy regulation. To improve prediction capabilities, more and more studies have introduced deep learning methods to model spatial dependencies and temporal dynamics in meteorological data. [PERSON] et al. [11] proposed the GRAST-Frost model, which combines the spatiotemporal attention mechanism with the Internet of Things platform to integrate multi-source data of the target site and 10 surrounding sites, and supports 6, 12, 24, and 48-h low temperature and frost forecasts. [PERSON] et al. [19] designed a GNN-based regional heatwave prediction method that can provide accurate real-time high temperature warnings while maintaining low data collection and computing costs, and reveal the spatiotemporal propagation patterns of heat waves and regional climate causal relationships. The authors point out that the framework can also be extended to identifying and forecasting various other severe or intricate weather phenomena. [PERSON] et al. [20] conducted an empirical study on South Africa, comparing the performance of low-rank weighted graph neural network and Graph WaveNet in predicting the maximum temperature of 21 weather stations, and compared with LSTM and temporal convolutional network (TCN). [PERSON] et al. [21] proposed a spatiotemporal adaptive attention graph convolution model for short-term PM2.5 prediction at the city level, and achieved leading performance on multiple real datasets such as Beijing, Tianjin, and London. For the problem of wind speed modeling, [PERSON] et al. [22] proposed a multidimensional spatiotemporal graph neural network, which integrates local and neighboring node wind speed data, effectively improving the accuracy of local wind speed prediction. [PERSON] et al. [23] constructed an undirected graph network with wind power stations as nodes, and achieved robust prediction of short-term wind speed by learning the spatiotemporal characteristics of wind speed and wind direction. To improve the modeling ability of regional interactions, [PERSON] et al. [10] proposed HiSTGNN, introduced an adaptive graph learning mechanism, constructed a hierarchical structure including a global graph (modeling inter-regional dependencies) and a local graph (modeling the relationship between variables within the region), combined graph convolution and gated temporal convolution to extract complex spatial dependencies and long-term meteorological trends, and enhanced the information flow between the two  
Figure 1: Overview of the HyMePre two-stage framework: spatial–temporal decoupled pretraining and hypergraph-based forecasting. layers of graphs through a dynamic interaction mechanism. [PERSON] et al. [PERSON] et al. (2018) proposed a WGC-LSTM model that couples weighted graph convolution and long short-term memory network to effectively model spatial relationships in order to deal with the nonlinearity and spatiotemporal autocorrelation problems in meteorological data. [PERSON] et al. [PERSON] et al. (2018) proposed the PaGN framework, which improves physical consistency and prediction accuracy by integrating sparse spatial observation data with high-order physical numerical methods. [PERSON] et al. [PERSON] et al. (2018) proposed a group-aware GNN that builds both individual city graphs and city-cluster graphs to represent explicit and implicit spatial relationships among urban areas. Their model incorporates differentiable grouping mechanisms to autonomously identify urban clusters, thereby enhancing national-scale air quality forecasting performance. In addition, to deal with the problem of multi-task coupling, [PERSON] et al. [PERSON] et al.