In this paper,in order to reduce or remove the noise components included in observations of surface temperature in China as much as possible and improve the quality of observations,a new single station quality control algorithm is proposed.The algorithm combines Ensemble Empirical Mode Decomposition (EEMD) and Cubic Exponential Smooth (CES).First,the EEMD method is used to decompose the temperature observations into a series of relatively stable intrinsic mode functions.Next,based on the energy density and the correlation criterion,the target component is analyzed and screened out to complete the data reconstruction.Finally,the CES method is used to establish a single station quality control model with the reconstructed data,and form an EEMD-CES quality control model.In order to test the feasibility and applicability of this method,this paper selects the surface hourly temperature observations from nine stations throughout the country for quality control in 2008.The results are then compared with the quality control effects of the traditional single station quality control method,empirical mode decomposition method,and cubic exponential smoothing method.The experimental results show that the quality control method based on EEMD-CES can effectively mark the suspicious value of data,and has a higher error detection rate and stronger adaptability than the traditional method.