Deployment of wave energy converters (WECs) relies on consistent and accurate wave resource characterization, which is typically achieved through numerical modeling using deterministic wave models. The accurate predictions of large-wave events are critical to the success of wave resource characterization because of the risk on WEC installation, maintenance, and damage caused by extreme sea states. Because wind forcing is the primary driver of wave models, the quality of wind data plays an important role in the accuracy of wave predictions. This study evaluates the sensitivity of large-wave prediction to different wind-forcing products, and identifies a feasible approach to improve wave model results through improved wind forcing. Using a multi-level nested-grid modeling approach, we perform a series of sensitivity tests at four representative National Data Buoy Center buoy locations on the U.S. East and West Coasts. The selected wind-forcing products include the Climate Forecast System Reanalysis global wind product and North American Regional Reanalysis regional wind product as well as the observed wind at the buoys. Sensitivity test results indicate a consistent improvement in model predictions for the large-wave events (e.g., >90th percentile of significant wave height) at all buoys when observed-wind data were used to drive the wave model simulations.