TIDE: Tendency-Informed Dynamic Encoding for Detail-Preserving Global Weather Forecasting

Skillful global weather forecasting is essential for agricultural production, energy management, and disaster mitigation. Despite the rapid progress of data-driven forecasting models, two challenges remain insufficiently addressed. First, historical atmospheric states are often compressed through static or weakly conditioned temporal aggregation, limiting the representation of persistent evolution, first-order tendencies, and accelerating changes. Second, repeated spatial downsampling and autoregressive rollout tend to suppress fine-scale structures, resulting in progressively over-smoothed forecasts. To address these challenges, we propose TIDE, a tendency-informed and detail-preserving framework for global weather forecasting. Its State-Tendency-Acceleration Fusion module constructs first- and second-order temporal differences and adaptively integrates them with the latest state across the bottleneck and pathways, therefore better capturing persistent evolution and accelerating atmospheric changes. To preserve spatial structures, TIDE employs a multiresolution analysis and reconstruction architecture that learns temporally evolving detail memories and selectively restores them through gated decoding. Experiments on ERA5 dataset demonstrate that TIDE achieves state-of-the-art performance in global weather forecasting, establishing a new paradigm in this field.