The availability of sea surface wind conditions with a high-resolution (HR) space-time sampling is a critical issue for a wide range of applications. Currently, no observation systems nor model forecasts provide relevant information with a high sampling rate in both space and time. Synthetic aperture radar (SAR) satellite systems deliver HR sea surface fields, with a spatial resolution below 0.01., but they are also characterized by a large revisit time up 7 to 10 days for temperate zones. Meanwhile, operational model predictions typically involve a high temporal resolution (e.g., every 6 h), but also a low spatial resolution (0.5 degrees). With a view to leve-raging both data sources, we investigate statistical downscaling schemes. In this study, a new model based on a machine learning method, namely support vector regression (SVR), is built to reconstruct HR sea surface wind fields from low-resolution operational model forecasts. The considered case study off Norway demonstrates the relevance of the proposed SVR model. It outperforms state-of-the-art approaches [namely, linear, analog, and empirical orthogonal function (EOF) downscaling models] in terms of mean square error. It also realistically reproduces complex space-time variabilities of the observed SAR wind fields. We further discuss the SVR model as a generalization of the popular linear and analog models.