The statistical predictability of wintertime (December through February) monthly-mean sea surface winds (both vector wind components and wind speed) in the subarctic Northeast Pacific off of the West Coast of Canada is considered, in the context of surface wind downscaling. Predictor fields (zonal wind, meridional wind, wind speed, and temperature) are shown to carry predictive information on large scales (both vertical and horizontal) that are well simulated by Numerical Weather Prediction and Global Climate Models. It is found that in general the monthly-mean vector wind components are more predictable by indices of the large-scale flow than is the monthly-mean wind speed, with no systematic vertical variation in predictive skill for either across the depth of the troposphere. The difference in predictive skill between monthly-mean vector wind components and wind speed is interpreted in terms of an idealised model of the vector wind speed probability distribution, which demonstrates that for the conditions in the subarctic Northeast Pacific the sensitivity of mean wind speed to the standard deviations of vector wind component fluctuations (which are not well predicted) is greater than that to the mean vector wind components. It is demonstrated that this sensitivity is state-dependent, and it is suggested that monthly mean wind speeds may be inherently more predictable in regions where the sensitivity to the vector wind component means is greater than that to the standard deviations. It is also demonstrated that daily wind fluctuations (both vector wind and wind speed) are generally more predictable than monthly-mean variability, and that monthly-averages of the predicted daily winds generally represent the monthly-mean surface winds better than do the predictions directly from monthly-mean predictors.