The availability of sea surface wind conditions with a high-resolution 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 both in space and time. Synthetic Aperture Radar (SAR) satellite systems deliver high-resolution sea surface fields, with a spatial resolution below 0.01◦, but they are also char- acterized 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◦). With a view to leveraging 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 high-resolution 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.