Many marine activities, such as designing ocean structures and planning marine operations, require the characterization of sea state climate. This study investigates the statistical relationship between wind and sea states, considering its spatiotemporal behavior. A transfer function is established between wind fields over the North Atlantic (predictors) and the significant wave height (predictand) in a location in the Bay of Biscay off the French coast. The developed method takes into consideration both wind seas and swells by including local and global predictors. The global predictors’ spatiotemporal structure is defined to account for the non-local and non-instantaneous relationship between wind and waves, using a fully data-driven approach. Weather types are constructed using a regression guided-clustering method, and the resulting clusters correspond to different wave systems (swells and wind seas). Then, in each weather type, a penalized linear regression model is fitted between the predictor and the predictand. The validation analysis proves the model’s skill in predicting the significant wave height (RMSE = 0.27m); furthermore, the interpretability of the model is discussed.