Nowadays,ensemble forecasting has become the main support for operational weather forecasting.However,due to the limitations and imperfections of the numerical model itself,as well as the limitations of the ensemble forecast system in terms of initial perturbation schemes,ensemble size,etc.,the forecast results are generally biased.In addition,different forecasting models usually have different physical parameterization schemes,initial conditions,etc.,resulting in different forecasting capabilities.Therefore,how to correct the forecast deviation and how to make full and effective use of forecast information from different models to obtain more accurate weather forecasts has received extensive attention.In recent years,using statistical theory and forecasting diagnosis,multimodel ensemble forecasting technologies based on multiple ensemble prediction systems have been rapidly developed,and has become a statistical post-processing method to effectively eliminate forecast deviation and improve weather forecasting skills.For the three most basic surface meteorological variables (i.e.,temperature,precipitation and wind),the widely used multimodel ensemble technologies such as ensemble mean (EM),bias-removed ensemble mean (BREM),superensemble (SUP),Bayesian model averaging (BMA),and ensemble model output statistics (EMOS) are first introduced from the perspective of deterministic forecasting and probabilistic forecasting.Finally,this paper discusses the issues that need to be paid attention to when using and developing multimodel ensemble technologies,including considering the number of participating models,developing precipitation and wind speed classification forecast models,and developing new multimodel ensemble technologies based on machine learning.