Climate change is increasing the frequency and the magnitude of extreme meteorological events, including windstorms, which pose a growing threat to the forests and the ecosystem services they provide. In mountainous regions such as the Alps, forests play a crucial role in protecting against gravitational hazards, a function that may be compromised due to wind-induced damage. Identifying forests most vulnerable to extreme windstorms is therefore essential to enhance their resilience. In this study, we assess the forest wind vulnerability of the Cordevole catchment (~ 700 km2), using high-resolution LiDAR data to extract detailed stand and individual tree-level characteristics. These data serve as inputs for the semi-mechanistic ForestGALES model, which estimates the forest wind vulnerability. The probability and the magnitude of wind damages are calculated using km-scale Convection Permitting Models (CPMs) from CORDEX-FPS on Convective Phenomena over Europe and the Mediterranean (FPS Convection). Specifically, we used wind data from the CPMs ensemble for both historical (1996-2006) and future (2090-2099 conditions. The resulting maps show the likelihood of forest wind damage under both current conditions and RCP 8.5 future scenario, identifying the areas with higher exposure. The methodology to derive the forest wind susceptibility was validated for a smaller area respect the Cordevole catchment by using the observed damages of the Vaia storm, occurred in 2018. The final hazard maps classify wind disturbance hazard into three levels (low, medium, high). The spatial analysis highlights areas where active forest operations are necessary to improve the forest resistance. At the scale of the Cordevole catchment the extent of the cumulative hazard of forest damages will be double in the future compared to historical conditions. This study underscores the importance of integrating high-resolution forest and climate data to assess the vulnerability of natural resources against windstorms. By combining detailed forest structure data with advanced climate projections, the adopted approach provides useful maps for forest management and climate adaptation planning.