摘要Storm surges in the Western North Pacific cause significant economic damage and loss of life,highlighting the need for accu-rate storm surge predictions.This study evaluated four storm surge models:the Global Tide and Surge Model(GTSMv3.0),ERA20C neural network(ERA20C_nn),ERA20C multiple linear regression(ERA20C_ml),and 20th Century Reanalysis multiple linear regression(20CR_ml),using data from 160 tidal stations.The results show that the ERA20C_nn model out-performed others,with the highest correlation to tide-gauge observations.The GTSMv3.0 model follows closely,although slightly less accurate.The ERA20C_ml and 20CR_ml models were less effective,especially in predicting extreme surges.The ERA20C_nn model also provided more reliable estimates for 100-year return surge levels,outperforming other models.These findings suggest that neural network-based models,particularly ERA20C_nn,are better suited for assessing coastal flood risks in the region.