## 1 Introduction  
Weather variations have long been one of the most critical external factors influencing societal operations and economic activities. In domains such as agricultural planning, transportation scheduling, energy dispatching, urban management, and public safety, timely and accurate access to meteorological information is a fundamental prerequisite for effective decision-making. The increasing frequency of extreme weather events under climate change further underscores the need for more accurate and robust forecasting systems [1; 2]. Consequently, developing scientific and reliable short-term weather forecasting systems is of profound significance, as it supports disaster mitigation, resource optimization, the protection of lives, and the advancement of sustainable development. In the context of weather forecasting tasks, two primary methodological paradigms are currently employed: traditional numerical weather prediction (NWP) models [3; 4] based on physical principles, and data-driven approaches that have gained traction in recent years. The former rely on atmospheric dynamics and thermodynamics to simulate future weather states from initial conditions, offering strong physical interpretability and theoretical rigor. However, these models tend to be computationally intensive and highly sensitive to input precision. In contrast, data-driven methods leverage historical observations to learn statistical patterns among meteorological variables, often through deep neural networks. These methods excel at modeling complex spatiotemporal dependencies and enable rapid inference. Particularly in scenarios with abundant data, data-driven approaches have demonstrated impressive performance in short-term and localized forecasting, making them a valuable complement to traditional models. Despite the significant progress achieved by deep learning models, their application in weather forecasting still faces several challenges, particularly in leveraging large-scale unlabeled meteorological data. Supervised models require high-quality labeled inputs, yet real-world weather observations often suffer from noise, missing values, and regional inconsistencies. Self-supervised pretraining has emerged as an effective solution, enabling models to extract robust patterns from raw meteorological time series without relying on labeled data. Among various strategies, masked modeling, originally developed in the domains of natural language processing [5] and computer vision [6], has demonstrated strong potential for time series forecasting [7]. These methods reconstruct randomly masked segments of input data, promoting the learning of contextual and structural representations. In meteorological applications, decoupling spatial and temporal masking has proven particularly beneficial, as it encourages the model to separately capture regional correlations and temporal dynamics [8; 9]. Decoupling spatial and temporal masking not only improves generalization and robustness but also provides transferable representations for downstream prediction tasks. Beyond representation learning, modeling spatial interactions among multiple stations is a central challenge in weather forecasting. Conventional graph neural networks (GNNs) have shown strong ability to capture pairwise spatial dependencies by leveraging station graphs [10; 11]. These methods naturally encode spatial proximity and correlation structure, offering a principled framework to handle spatially distributed data. Building on this, several large-scale GNN-based models have recently achieved major breakthroughs in global weather prediction. GraphCast [12] models atmospheric evolution using GNNs on a sphere mesh and demonstrates superior medium-range forecasting accuracy compared to traditional NWP systems. NeuralGCM [13] augments physics-based general circulation models with neural components to improve simulation fidelity while maintaining physical constraints.