Abstract: With the extensive use of ensemble forecasts, various novel ensemble statistical postprocessing methods have emerged in recent years. This review tries to summarize these methods comprehensively, including univariate and multivariate ensemble statistical postprocessing methods. The univariate methods can be classified as parametric methods based on certain mathematical distributions (such as logistic regression, nonhomogeneous regression, Bayesian model averaging, and ensemble dressing) and flexible nonparametric methods. Many techniques belong to the nonparametric type, including rank histogram calibration, reliability curve calibration, decaying average bias correction, quantile mapping, quantile regression, probability matching, frequency matching, optimal percentile, optimal score, member-by-member calibration, analog, neighborhood, object-based probabilistic forecast and pseudo-precipitation methods. As an extension of univariate methods, multivariate ensemble statistical postprocessing methods need to construct multivariate dependence structure, including the parametric copula, the nonparametric ensemble copula coupling, and Schaake shuffle methods. Then, multimodel ensemble and machine learning methods are introduced. In addition, limits and validity of the widely used ensemble postprocessing methods are discussed.