This study focuses on the generation of high-resolution wind forecasts over East China and its surrounding regions (110°—130°E,20°—40°N) for the period from January to April 2020,utilizing wind forcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF),the Mesoscale component of the Global and Regional Assimilation and Prediction Enhanced System (GRAPES-Meso),the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP),and the Global Forecast System of the Global and Regional Assimilation and Prediction System (GRAPES-GFS).Various interpolation techniques,including bilinear interpolation,inverse distance weighted interpolation,kriging interpolation,and cubic spline interpolation,were employed to create downscaling forecasts spanning from 0 to 72 hours.These high-resolution forecasts aim to cater to the specific needs of airports and their terminal areas.Furthermore,this study encompasses multimodel ensemble forecasts of high-resolution wind fields.The results reveal that inverse-distance weighted interpolation outperforms other interpolation schemes for horizontal wind forecast interpolation.Leveraging the augmented complex extended Kalman Filter (ACEKF) for multimodel ensemble forecasts substantially reduces root-mean-square errors (RMSEs) in wind field predictions.Notably,whether concerning surface winds or high-level winds,the ACEKF forecasts exhibit significant superiority compared bias-removed ensemble mean (BREM) forecasts and individual models,as evidenced by lower RMSEs.Examining wind forecasts at three prominent airports in East China—Shanghai,Qingdao,and Xiamen—reveals that ACEKF forecasts not only feature reduced RMSEs compared to BREM,ECMWF,and GRAPES-GFS forecasts but also display consistent performance across varying altitudes.This heightened forecast stability distinguishes ACEKF forecasts from BREM and individual model forecasts.