MS-SSTNet: A Scale-Aware Spatiotemporal Learning Framework for Satellite SST Forecasting via Iterative Multiscale Decomposition and Dual-Window Modelling

Accurate sea surface temperature (SST) forecasting underpins operational oceanography and climate surveillance, yet it remains constrained by the inherent multiscale spatiotemporal variability. Existing deep learning methods often struggle to identify the physical hierarchies of SST fields, typically treating them as homogeneous inputs and thus failing to decouple large-scale coherent structures from transient features effectively. To bridge this gap, this study introduces MS-SSTNet, a scale-aware framework designed for spatiotemporal SST forecasting that leverages iterative multiscale decomposition. By iteratively distilling SST fields into hierarchical spatial modes and their principal components (PCs), the architecture facilitates a rigorous scale-decoupling representation of ocean dynamics. A dual-window temporal module is then integrated to characterize the coupling between long-term persistent trends and short-term stochastic fluctuations. Evaluations using satellite-derived SST products over the South China Sea (SCS) demonstrate that MS-SSTNet achieves robust 10th day forecast skill, yielding an overall spatiotemporal average MAE of 0.3031 °C, an average RMSE of 0.4062 °C, and an average ACC of 0.7973 across the entire 1–10 day forecast horizon. Ablation studies further underscore the indispensability of multiscale decomposition and dual-window integration in enhancing forecast fidelity across diverse spatiotemporal scales.