Dual-Scale Temporal Fusion Reveals Structured Predictability in Subseasonal-to-Seasonal Temperature Prediction

2026-05-07

AI Summary

This study addresses the pronounced spatiotemporal heterogeneity in the reliability of subseasonal-to-seasonal (S2S) temperature forecasts, which cannot be adequately captured by conventional approaches relying solely on lead time. The authors propose a dual-scale learning framework that disentangles calendar-aligned climatic background states from lead-time-matched recent weather evolution. By incorporating a spatially adaptive fusion mechanism and topology-aware constraints, the model jointly represents multiscale temporal components, spatial heterogeneity, and large-scale climate modes. The approach substantially enhances forecast stability at 30–90-day lead times, particularly over high-latitude regions and complex terrain during winter. Moreover, the learned fusion weights reveal a reorganization of predictability governed by seasonal and geographic factors, thereby reshaping the conceptual paradigm of S2S predictability.

Abstract

Subseasonal-to-seasonal (S2S) temperature forecasts, spanning several weeks to a few months, are critically needed in agriculture practice, energy planning, and extreme-weather induced risk management, yet their reliability varies substantially across seasons and regions. Forecast skill is often attributed primarily to lead time, but this perspective does not fully explain the spatiotemporal patterns of predictability. Here we show that S2S predictability is organized across interacting temporal components, spatial heterogeneity, and large-scale pattern coherence, and that this structure can be explicitly characterized and exploited. We develop a dual-scale learning framework that separates calendar-aligned historical climate context from lead-time matched recent weather evolution, combining them through spatially adaptive fusion to enable stable temperature forecasts across the 30 to 90-day window. The learned fusion weights reveal that the balance between these two temporal scales shifts systematically with season and geography: during winter, interannual context dominates over high latitudes and complex terrain where forecast is the most difficult, while summer predictions reflect a more balanced temporal contribution across the domain. This spatially explicit reorganization of predictability, rather than simple lead-time decay, emerges as the primary determinant of forecast skill within the subseasonal window. Topology-aware structural constraints further improve spatial coherence of predicted temperature fields, stabilizing large-scale pattern organization particularly over complex terrain. These results reframe S2S predictability as a structured, multi-scale phenomenon, providing a more interpretable foundation for improving forecast systems and informing their use in practice.

Problem

• subseasonal-to-seasonal prediction
• temperature forecast
• predictability
• spatiotemporal patterns
• forecast skill

Innovation

• dual-scale temporal fusion
• subseasonal-to-seasonal prediction
• spatially adaptive fusion
• structured predictability
• topology-aware constraints