Title: MMRN: A Multi-scale Multi-task Residual Network for Seasonal Climate Forecasting
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: global seasonal climate forecasting

Abstract: Seasonal climate forecasting has high social and economic benefits in many fields. Although spatial multi-scale modeling of the coupled ocean-atmosphere climate system is a promising direction for seasonal forecasting, two key challenges limit it further development: 
(1) Feature entanglement across scales and variables not only hinders the disentangling and learning of multi-scale features but also introduces heterogeneous interference.
(2) Hierarchical error propagation causes errors to accumulate at the final forecasting scale, compromising the forecast skill. To this end, we propose Multi-scale Multi-task Residual Network (MMRN) for seasonal climate forecasting. Specifically, to mitigate the feature entanglement, a group-wise variables embedder module is first introduced to independently embed the ocean and atmosphere variables, then a residual multi-scale feature extractor module is designed to extract disentangled multi-scale features. These features are then modeled to generate forecast at each scale. In addition, a multi-task learning mechanism is employed in the learning process by imposing scale-specific supervision to curb the hierarchical error propagation. Extensive experiments on the reanalysis dataset demonstrate that MMRN achieves state-of-the-art performance compared to the advanced deep-learning based models. Code is available at https://anonymous.4open.science/r/MMRN-4FB5/.