Abstract The use of numerical guidance has become integral to the process of modern weather forecasting. Using various techniques, postprocessing of numerical model output has been shown to mitigate some of the deficiencies of these models, producing more accurate forecasts. The operational consensus forecast scheme uses past performance to bias-correct and combine numerical forecasts to produce an improved forecast at locations where recent observations are available. This technique was applied to forecasts of significant wave height (Hs), peak period (Tp), and 10-m wind speed (U10) from 10 numerical wave models, at 14 buoy sites located around North America. Results show the best forecast is achieved with a weighted average of bias-corrected components for both Hs and Tp, while a weighted average of linear-corrected components gives the best results for U10. For 24-h forecasts, improvements of 36%, 47%, and 31%, in root-mean-square-error values over the mean raw model components are achieved, or 14%, 22%, and 18% over the best individual model. Similar gains in forecast skill are retained out to 5 days. By reducing the number of models used in the construction of consensus forecasts, it is found that little forecast skill is gained beyond five or six model components, with the independence of these components, as well as individual component’s quality, being important considerations. It is noted that for Hs it is possible to beat the best individual model with a composite forecast of the worst four.