###### Abstract
Currently, commonly used parametric typhoon wind field models can be classified into two categories: gradient wind balance-based models (including the Batts wind field model [5] and the Yan Meng wind field model [20]) and momentum balance-based models (comprising the Shapiro wind field model [21] and the CE wind field model (the U.S. Army Corps of Engineers Wind Field Model) [22]) [23]. The Yan Meng wind field model features a complete analytical solution that enables direct calculation of wind speeds at specific locations. Compared with the Shapiro and CE wind field models, the proposed model has superior computational efficiency. Moreover, the Yan Meng model incorporates a more physically meaningful boundary layer model than the empirical reduction factor used in the Batts model does and has proven its reliability in computational accuracy. This study utilizes the Northwest Pacific tropical cyclone best-track dataset from the China Meteorological Administration-Shanghai Typhoon Institute (CMA-STI) to obtain historical typhoon samples. First, the simulated circle method is applied to screen typhoon events with potential impacts on the target region, followed by probabilistic distribution fitting of key parameters (e.g., central pressure deficit, translation speed, and radius of maximum winds). Second, the Monte Carlo method is employed to generate synthetic typhoon events through random sampling of the fitted parameters, and a straight-line track model is adopted to simulate typhoon trajectories. For landfalling typhoons, an exponential decay model is introduced to characterize their postlandfall intensity evolution. The [PERSON] gradient wind model is subsequently implemented to calculate the maximum wind speed sequences induced by synthetic typhoons in the target area. Finally, the generalized extreme value distribution (GEV) is applied to fit the wind speed sequences, enabling the prediction of extreme wind speeds for specific return periods. These results are compared with code-specified design wind speeds to validate the methodology's robustness. The overall idea of using the typhoon straight-line path model and Monte Carlo simulation method to obtain the typhoon extreme value sequence is shown in Figure 1.  
Therefore, this study aims to address the lack of localized probabilistic models for typhoon-induced extreme wind speed estimation in coastal regions of China, especially under conditions of limited historical observations. Specifically, we develop a modular simulation framework that integrates a straight-line typhoon path model, a physically based wind field model, and a Monte Carlo stochastic simulation to predict extreme wind speeds corresponding to various return periods for a target location. Unlike previous studies that focused primarily on full-track models or used empirical adjustments, this research systematically analyses the sensitivity of key parameters and simulation assumptions (e.g., data selection domain and genesis point generation) to quantify their effects on wind speed prediction. By comparing the simulation results with national code-specified wind speeds, this study also investigates the conservatism of current design standards. These analyses collectively contribute to improving the reliability and applicability of typhoon hazard assessment models in data-scarce coastal regions.