Tropical cyclones (TCs) are complex and powerful weather systems, and accurately forecasting their path, structure, and intensity remains a critical focus and challenge in meteorological research. In this paper, we propose an Attention Spatio-Temporal predictive Generative Adversarial Network (AST-GAN) model for predicting the temporal and spatial distribution of TCs. The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals, emphasizing the cyclone's center, high wind-speed areas, and its asymmetric structure. To effectively capture spatiotemporal feature transfer at different time steps, we employ a channel attention mechanism for feature selection, enhancing model performance and reducing parameter redundancy. We utilized High-Resolution Weather Research and Forecasting (HWRF) data to train our model, allowing it to assimilate a wide range of TC motion patterns. The model is versatile and can be applied to various complex scenarios, such as multiple TCs moving simultaneously or TCs approaching landfall. Our proposed model demonstrates superior forecasting performance, achieving a root-mean-square error (RMSE) of 0.71 m s-1 for overall wind speed and 2.74 m s-1 for maximum wind speed when benchmarked against ground truth data from HWRF. Furthermore, the model underwent optimization and independent testing using ERA5 reanalysis data, showcasing its stability and scalability. After fine-tuning on the ERA5 dataset, the model achieved an RMSE of 1.33 m s-1 for wind speed and 1.75 m s-1 for maximum wind speed. The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets, maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs. (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(AST-GAN)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)(sic)15(sic)(sic)(sic)(sic)(sic)3(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)HWRF(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)HWRF(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(RMSE)(sic)0.71 m s-1, (sic)(sic)(sic)(sic)(sic)RMSE(sic)2.74 m s-1, (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic), (sic)(sic)(sic)(sic)(sic)ERA5(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)ERA5(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)RMSE(sic)(sic)(sic)(sic)1.33 m s-1 (sic)1.75 m s-1.
