Nowadays, ensemble forecasting has become the main support for operational weather forecasting. However, due to the limitations and imperfections of the numerical model itself, the forecast results are generally systematically biased. In addition, different forecasting models usually have different physical parameterization schemes, initial conditions, etc., resulting in different forecasting capabilities. Therefore, how to eliminate systematic biases 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 to effectively eliminate systematic biases and improve weather forecasting skills. For the three most basic surface meteorological variables (i.e., temperature, precipitation, 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, the issues that need to be paid attention to when using and developing multimodel ensemble technologies are discussed, including the consideration the number of participating models, the development of categorized precipitation and wind speed forecast models. Meanwhile, the combination of multimodel ensemble with machine learning deserves more investigation.