Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies. Current methods predominantly rely on limited spatiotemporal information from ERA5 data and neglect the causal relationships between these physical variables, failing to fully capture the spatial and temporal patterns required for intensity forecasting. To address this issue, we propose a Multi-modal multi-Scale Causal AutoRegressive model (MSCAR), which is the first model that combines causal relationships with large-scale multi-modal data for global TC intensity autoregressive forecasting. Furthermore, given the current absence of a TC dataset that offers a wide range of spatial variables, we present the Satellite and ERA5-based Tropical Cyclone Dataset (SETCD), which stands as the longest and most comprehensive global dataset related to TCs. Experiments on the dataset show that MSCAR outperforms the state-of-the-art methods, achieving maximum reductions in global and regional forecast errors of 9.52% and 6.74%, respectively. The code and dataset are publicly available at https://anonymous.4open.science/r/MSCAR.

通过将因果关系与大规模多模式数据相结合，我们提出了第一个全球台风强度自回归预测模型Multimodal multi-Scale Causal AutoRegressive model（MSCAR），并提供了迄今为止最长且最全面的与台风有关的全球数据集Satellite and ERA5-based Tropical Cyclone Dataset（SETCD）。实验结果表明MSCAR优于最先进的方法，在全球和地区预测误差上分别实现了最大减少9.52%和6.74%。

全球热带气旋强度预测的多模式多尺度因果自回归模型