  Weather forecasting plays a critical role in various sectors, driving
decision-making and risk management. However, traditional methods often
struggle to capture the complex dynamics of meteorological systems,
particularly in the presence of high-resolution data. In this paper, we propose
the Spatial-Frequency Attention Network (SFANet), a novel deep learning
framework designed to address these challenges and enhance the accuracy of
spatiotemporal weather prediction. Drawing inspiration from the limitations of
existing methodologies, we present an innovative approach that seamlessly
integrates advanced token mixing and attention mechanisms. By leveraging both
pooling and spatial mixing strategies, SFANet optimizes the processing of
high-dimensional spatiotemporal sequences, preserving inter-component
relational information and modeling extensive long-range relationships. To
further enhance feature integration, we introduce a novel spatial-frequency
attention module, enabling the model to capture intricate cross-modal
correlations. Our extensive experimental evaluation on two distinct datasets,
the Storm EVent ImageRy (SEVIR) and the Institute for Climate and Application
Research (ICAR) - El Ni\~{n}o Southern Oscillation (ENSO) dataset, demonstrates
the remarkable performance of SFANet. Notably, SFANet achieves substantial
advancements over state-of-the-art methods, showcasing its proficiency in
forecasting precipitation patterns and predicting El Ni\~{n}o events.
