Based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Mesoscale of the Global and Regional Assimilation and Prediction System (GRAPES-Meso), the Global Forecast System (GFS) of National Centers for Environmental Prediction (NCEP), and the Global Forecast System of the Global and Regional Assimilation and Prediction System (GRAPES-GFS) wind forecast data over East China and surrounding areas (110°~130°E, 20°~40°N) from January to April , 2020, the bilinear interpolation, inverse distance weighted interpolation, kriging interpolation and cubic spline interpolation were applied to produce the 0~72h downscaling forecasts in order to provide the high-resolution forecast service for the airports and their terminal areas. In addition, multimodel ensemble forecasts of the high-resolution wind have been conducted. The results show that the inverse distance weighted interpolation is the best interpolation scheme for the horizontal interpolation of the wind forecasts. The augmented complex extended Kalman Filter (ACEKF) based multimodel ensemble forecasts further reduce the root-mean-square errors (RMSEs) of the wind fields. No matter for the surface winds or high-level winds, ACEKF forecasts are significantly superior to those of bias-removed ensemble mean (BREM) and individual models in terms of their RMSEs. The surface and high-level wind forecasts at three airports in East China, namely, Shanghai, Qingdao and Xiamen, show that the RMSEs of the ACEKF forecasts are not only smaller than those of BREM, ECMWF and GRAPES-GFS forecasts, but also less variable with altitudes, the performance of the wind forecasts is more stable than that of BREM and individual model forecasts.