This paper explores a statistical regression approach to downscale large-scale global circulation model output to the wind speed distribution at the hub-height of tall wind turbines. The methodology is developed for Cabauw, using observational, ERA-Interim and ECHAM5 data. The regression analysis is based on the parameters of the probability distribution functions (pdfs) and includes a variable evaluation prior to the development of the statistical models. During winter ECHAM5 performs very well in representing the ERA-Interim wind speed pdf at hub-height. However, during summer, the hub-height wind speed pdf is not well represented by ECHAM5. A regression analysis shows that during summer-day the hub-height wind speed is strongly linked to the wind speed at higher, skillfully represented levels. The summer-day hub-height wind speed can therefore be skillfully predicted using wind speed pdf parameters of higher levels (R2 of the model using 500 m wind speed scale parameter as a predictor is 0.84). During the summer-night, the stable boundary layer is much shallower and the statistical model shows that solely the higher level wind speed is not able to skillfully predict the hub-height wind speed pdf (R2 of 0.59). Including temperature information in the downscaling model substantially improves the prediction of the summer-night hub-height wind speed pdf (R2 adjusted of 0.68).