Current wind resource assessments primarily rely on historical wind climate data and typically assume persistence in the wind conditions for the future climate. However, climate change influences the large-scale atmospheric circulation and thus might impact the available wind resource at specific sites. These possible changes in the wind climate are represented in climate model simulations. In this work, we introduce a methodology that integrates climate model ensemble data into the long-term referencing process using the Measure-Correlate-Predict (MCP) framework to assess future wind speed variations at specific sites. Seven sites across Europe with measurements near modern wind turbine hub heights (100 m) are analyzed. The approach incorporates the ERA5 reanalysis dataset and an ensemble of ten global climate models from the Coupled Model Intercomparison Project (CMIP6) under intermediate and very high green house gas emission scenarios (SSP2-4.5 and SSP5-8.5). The methodology is validated against historical data and the results indicate that the approach reliably corrects climate model data to the specific site conditions. The bias correction reduces discrepancies between climate model outputs and observations, within all seasons. Depending on the site and climate model, future projections indicate a decrease in summer wind speeds ranging from <span>−</span>0.1 to <span>−</span>0.3 ms<sup><span>−</span>1</sup> by mid-century and up to <span>−</span>0.4 ms<sup><span>−</span>1</sup> by century’s end under SSP2-4.5, with slightly larger declines under SSP5-8.5. Conversely, a slight increase in winter wind speeds is observed, accompanied by greater uncertainty among models. Overall, the proposed methodology provides a robust, data-driven basis for improved future wind resource estimation, supporting investment decisions and enhancing understanding of risks under climate change.