Abstract: Accurately assessing the extreme wind speeds of typhoons is of great significance for ensurning the wind-resistant safety of important engineering structures in typhoon-affected areas.A typhoon track and intensity model based on the conditional generative adversarial networks (GAN) to facilitate refined analysis of extreme typhoon wind speeds is developed in the present study. The model begins by treating the 6-hour changes in typhoon translational speed and intensity as conditional random variables, which are determined by certain state variables, such as the latitude and longitude of the typhoon center, and environmental variables, such as sea surface temperature. Then, the conditional GAN is applied, where fully connected neural networks are used for both the generator and discriminator to model the 6-hour changes in translational speed and intensity. The neural networks are trained using meteorological data, including the best track data from the China Meteorological Administration and the 20th Century Reanalysis (CR) dataset. The performance of the typhoon track and intensity model is validated through comparisons with historical records, confirming that the models can effectively capture key characteristics of historical typhoon trajectories as well as the evolution of their intensity. The models are also shown to accurately reproduce essential statistical features of typhoon paths, such as mean values, standard deviations, and probability distribution functions for key parameters in localized typhoon paths. Finally, the developed typhoon model is applied to analyze extreme wind speeds along the southeastern coastal regions of China, and the results are compared with other research findings and guideline recommendations, providing a comprehensive evaluation of the model's accuracy and relevance in forecasting extreme wind events associated with typhoons. This model offers a more precise tool for understanding typhoon dynamics, potentially improving early warning systems and risk assessment for coastal regions.