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Future energy systems increasingly rely on weather-driven variable renewable energy (VRE) sources. As a result, the accuracy, resolution, and statistical consistency of meteorological inputs have become key considerations in energy system modelling (ESM). In particular, wind power estimates strongly depend on local wind speed characteristics, including both distributional properties and temporal variability. However, existing wind datasets at continental to national scale often lack sufficient spatial detail, exhibit systematic or statistical biases, or are insufficiently validated against observations. As a result, substantial uncertainty is introduced into wind energy assessments and system-level analyses. To address these limitations, we develop a framework for generating high-resolution hourly wind speed time series for Europe by combining distributional information with statistical downscaling techniques. We estimate a two-parameter Weibull distribution for each region using linear regression across multiple gridded products, including the Global Wind Atlas, ERA5 and E-OBS. The distribution is then evaluated using leave-one-out cross-validation against station measurements. In a second step, we use the validated Weibull distributions to bias-correct and downscale existing wind speed time series using several statistical downscaling approaches. Using station data as an observational benchmark, we assess the accuracy of the reconstructed time series and quantify the structural uncertainty associated with wind speed inputs derived from gridded datasets. The resulting high-resolution, bias-corrected wind speed products provide more robust meteorological inputs for renewable energy system modelling, improving estimates of wind power generation potential and supporting more reliable long-term system planning across Europe.