With the development of automatic meteorological observation technology in China,approximately 70,000 automatic observation stations have been constructed across the country,and the automation of meteorological observation has been fully realized.Automatic observation technology causes the amount of meteorological observation data to increase rapidly,but how to improve the utilization rate of automatic observation data through quality control is of particular importance.This study uses a total of 168 temperature observation data of automatic ground stations,beginning from 00:00 to 23:00 BST on December 1,2019,provided by Jiangsu meteorological bureau,along with the temperature grid data in the ECMWF Reanalysis V5 (ERA5) Reanalysis data of European Centre for Medium-range Weather Forecasts (ECMWF).Next,by combining the general quality control method and Empirical Orthogonal Function (EOF) quality control method,this paper establishes a quality control method for surface temperature data with high spatial and temporal resolution.Next,real data quality control experiments on surface temperature observation of automatic stations in central and eastern China are conducted to verify the effectiveness of the new method.The results show that,according to the characteristics of the automatic station high density,by choosing the appropriate analysis area,the EOF analysis method can effectively extract organized observation system information,so as to ensure that the remaining information better meet the random distribution characteristics.After this,abnormal observations can be effectively removed according to the probability distributions,which in turn can help avoid the impact of changes in the weather.