In order to improve the decomposition ability and decomposition accuracy of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN）, and solve the problem of noise residual in CEEMDAN, an improved CEEMDAN method called meanoptimized ensemble empirical mode decomposition with adaptive noise (MEEMDAN) is proposed. MEEMDAN introduces different weights in the process of iteration screening. Based on the minimum orthogonality, the optimal IMF is selected from the decomposition results under different weights as final decomposition result to ensure that the IMFs of each order are globally optimal. The simulation results show that MEEMDAN is superior to CEEMDAN in decomposition ability and accuracy. At the same time, a new fault diagnosis method for rolling bearings combining MEEMDAN with maximum correlation kurtosis deconvolution (MCKD) is proposed and applied to the simulation and measured data analysis. The results show that, compared with the existing methods, the proposed method can extract fault characteristic frequency more accurately, and has more advantages in decomposition ability and interference suppression frequency.