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TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling

Summary

Global station weather forecasting (GSWF) is crucial for localized and extreme weather prediction. Existing methods rely heavily on short-term patterns, struggling with chaotic dynamics and partial observations. We propose TSSM, a triaxial state space model with history-enhanced temporal-variable-historical paradigm. It incorporates period-aligned historical data to capture long-term, large-scale periodic patterns. TSSM achieves SOTA on Weather-5K with 10% accuracy gain and 61% extreme event improvement. It excels in long-horizon forecasting (37.5% gain at 240h) and iterative settings (103.5% gain at 48h×5). Under 80% missing observations, TSSM retains >90% performance, demonstrating robustness for real-world deployment.

SourcearXiv Machine LearningAuthor: Songru Yang, Zili Liu, Tao Han, Ben Fei, Fenghua Ling, Lei Bai, Chang Liu, Xiangyang Ji, Zhenwei Shi, Zhengxia Zou
TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling
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[Submitted on 14 Jul 2026]

Title:TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling

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Abstract:Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions. Despite efforts to exploit look-back windows, existing methods show limited accuracy gains and struggle with extreme events and error accumulation. These limitations stem from overreliance on short-term patterns, which are insufficient to capture chaotic weather dynamics, especially under partial observations. To address this problem, we propose a novel Triaxial State Space Model (TSSM) with a history-enhanced Temporal-VariableHistorical paradigm, which incorporates period-aligned historical weather data to compensate for long-term, large-scale periodic, and full-window weather patterns beyond the temporal lookback window. Specifically, TSSM stacks historical samples into period-aligned batches, where forecasting is causally supported by historical and current observations. Temporal, variable, and historical scanning are designed to capture axial temporal dependencies, variable correlations, and historical evolution. This structure is hierarchically shared to model seasonal to extreme events while alleviating misalignment across historical patterns. TSSM achieves SOTA performance on Weather-5K, the largest station weather dataset to date, with 10% and 61% gains in accuracy and extreme event metrics, and obtains 95% best or second-best results on human-involved datasets. Its advantages are more pronounced in long-horizon and iterative forecasting, reaching a 37.5% gain at 240h and up to 103.5% under a 48h times 5 iterative setting. Moreover, TSSM retains > 90% performance under up to 80% missing observations, compared with

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Key points

  • TSSM uses period-aligned historical data to enhance short-term weather forecasting.
  • Achieves state-of-the-art on Weather-5K dataset with 10% accuracy and 61% extreme event gains.
  • Excels in long-horizon and iterative forecasting: 37.5% at 240h, 103.5% at 48h×5.
  • Robust to missing data: >90% performance under 80% observation loss.

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