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 was developed in the present study. The model began by treating the 6-hour changes in typhoon translational speed and intensity as conditional random variables, which were determined by certain state variables, (e.g., as the latitude and longitude of the typhoon center) and environmental variables (e.g., as sea surface temperature). Then, the conditional GAN was applied, where fully connected neural networks were used for both the generator and discriminator to model the 6-hour changes in translational speed and intensity. The neural networks were 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 was validated through comparisons with historical records, confirming that the models could effectively capture key characteristics of historical typhoon trajectories as well as the evolution of their intensity. The models also demonstrated auurate reproduction of key statistical characteristics in typhoon track patterns, such as mean values, standard deviations, and probability distribution functions for key parameters in localized typhoon paths. Finally, the developed typhoon model was applied to analyze extreme wind speeds along the southeastern coastal regions of China, and the results were 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.