## 4 Conclusions  
This study developed a Monte Carlo simulation framework that integrates a typhoon straight-line trajectory model and the Yan Meng wind field model to predict extreme wind speeds across various return periods, with Guangzhou as a representative case. Comprehensive sensitivity analyses were conducted on spatial data inclusion criteria (simulated circle interior vs. exterior) and genesis point determination protocols. A probabilistic model for key typhoon parameters in Guangzhou was established, generating synthetic typhoon events spanning 1000 years. The results demonstrated the superior applicability of the Gumbel distribution for extreme wind speed modeling in this region while highlighting critical dependencies on spatial data selection and genesis logic. The proposed methodology forms a generalizable framework for typhoon hazard analysis in other coastal regions, providing substantial implications for typhoon disaster mitigation and enhancing structural safety assessments of typhoon hazards. The developed framework can serve as a practical tool for local authorities, disaster management agencies, engineering firms, and infrastructure planners, supporting wind hazard assessment and resilient infrastructure design in typhoon-prone coastal regions. Specifically, the predicted extreme wind speeds corresponding to various return periods can assist in refining the design of wind load specifications for critical structures, including offshore wind farms, transmission towers, bridges, and coastal ports. In addition, the model's modular structure allows for regional customization by adjusting input datasets and boundary conditions, offering a data-driven reference for local governments and standardization bodies seeking to update wind load codes on the basis of probabilistic hazard assessment. Owing to the selected models and methodologies, this study has certain limitations. (1) The straight-line typhoon path model employed in this research assumes climatic homogeneity within the simulation circle. As a result, it is only suitable for typhoon hazard analysis over relatively small regions. Its applicability becomes problematic when extended to larger spatial scales. Moreover, this model heavily relies on historical typhoon data, which are often sparse or unavailable in high-latitude areas, thereby limiting the reliability of analyses in those regions. (2) For the sake of computational simplification, the Monte Carlo simulations in this study generate each synthetic typhoon by sampling the key parameters only once. This approach differs from the actual evolution of real typhoons, in which key parameters may change continuously along the path. In future work, we plan to incorporate the inherent stochasticity of the typhoon development process by statistically analyzing the temporal variation in each key parameter and embedding such dynamics into the simulation framework. Additionally, geographical features--such as topography--will also be integrated into the model, enabling a more comprehensive evaluation of their impact on typhoon behavior. Moreover, uncertainty quantification was not performed in this study, as confidence intervals for the predicted extreme wind speeds were not provided. This limits the ability to assess the significance of differences between scenarios. In future research, uncertainty analysis methods, such as bootstrapping or Bayesian inference, will be incorporated to establish confidence intervals, improving the robustness of scenario comparisons. Methodology, X.Z. and W.X. ; Software, X.Z. and J.H. ; Validation, J.H. ; Investigation, Z.L. ; Formal analysis, Z.L. and X.Z. ; Writing--original draft, X.Z. and J.H. ; Writing--review and editing, Z.L. and W.X. ; Supervision, W.X. ; Project administration, W.X. ; Conceptualization, Z.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This work is Supported by the Headquarters Science and Technology Project of China Huaneng Group Co., Ltd. (No.