Toward accurate and efficient weather prediction using a dual-gated spatiotemporal attention network

Accurate weather forecasting plays a vital role in safeguarding human activities, mitigating the risks of extreme climate events, and supporting environmental policy and disaster preparedness. However, existing data-driven approaches often struggle to effectively model the complex spatiotemporal dynamics and multivariate dependencies inherent in meteorological systems, limiting their reliability and scalability. To address these challenges, we propose a novel dual-gated spatiotemporal attention network (DSANet) for multivariate weather prediction. DSANet integrates a convolutional self-attention hybrid module to jointly capture local and global spatial features, and a dual-gated channel-time module to model temporal patterns and inter-variable relationships. A wavelet-guided composite loss function is introduced to enhance prediction accuracy in fluctuating and dynamic weather regions. Extensive experiments on both global and regional datasets demonstrate that DSANet outperforms baseline models in terms of accuracy, with a mean absolute error of 1.78 K in 3-day lead-time global temperature forecasting. In addition, DSANet exhibits strong generalization and fast inference, making it well-suited for real-time and off-site forecasting. By significantly improving the accuracy, efficiency, and transferability of multivariate weather forecasting, DSANet provides a scalable and effective tool for next-generation climate intelligence and decision-making support systems.