Systematic Evaluation and Correction of Extreme Water Level in Global Storm Surge Simulation

Abstract Extreme sea levels resulting from storm surges are a major contributor to coastal flood risk and are often assessed using outputs from global storm surge models. However, these models exhibit systematic errors, particularly in data‐sparse regions. Here, we present a novel framework to improve simulated extreme water levels. The method applies quantile mapping to peaks‐over‐threshold extremes, is calibrated using paired observed and simulated annual maxima at tide gauges, and transfers the correction models from reference stations to nearby sites within a defined radius. Results show that the approach achieves consistent improvements within about 400 km of gauges, with median relative improvements of −0.84 in mean squared error and −0.60 in normalized root‐mean‐square error. Systematic errors are reduced most in high‐latitude, low‐wind regions. These findings highlight the transferability of the correction models and show that even sparse observations can improve extreme sea‐level estimates for global coastal hazard assessment.