Abstract: The accurate identification of precipitation type at ground level is one of the greatest difficulties for forecasters during winter. Special types of precipitation can be a threat to public safety and human health and can disrupt transportation and commerce, causing seriously loss of the economy. Winter precipitation may also cause serious disasters to aircraft navigation. In those situations, the consequences can be catastrophic, with heavy and prolonged freezing precipitation, collapsed power lines causing prolonged power outages, transportation networks of many types completely paralyzed, and even major long-term damage to infrastructure and vegetation in the most severe cases. Therefore, the accuracy of precipitation type is crucial for winter precipitation forecast. Accurate predictions from weather forecast models of timing (onset and duration), intensity, spatial extent, and phase (i.e., precipitation type) are crucial for decision-making and can help minimize the potential impacts. The research progress of precipitation type forecast in recent decades is investigated. The methods and techniques for predicting precipitation phases are reviewed systematically, which can be roughly divided into three categories. The index criterion methods are based on observations, numerical weather prediction weather predictions on thickness, area of warm atmosphere, significance level temperature, regression equation for vertical temperature profile, and model diagnosis. Some of those methods are highly dependent on the accuracy of the numerical model. The second type of methods are based on the microphysical processing scheme of numerical weather prediction model and ensemble prediction system. It includes microphysical scheme method and ensemble prediction method. The last type is the artificial intelligence prediction method including decision tree, artificial neural network, and deep learning etc. In recent years, sophisticated microphysical parameterizations schemes are widely used in high resolution regional forecast models, which help with precipitation-type prediction. The forecast accuracy of precipitation type model has been improved, which has become an important product support in precipitation type forecast. For instance, the precipitation type prediction product of ECMWF and the probabilistic prediction of precipitation type by ECMWF ensemble prediction. The probabilistic prediction has further improved the prediction skills compared with the deterministic model. However, even with such complex algorithms of NWP, correctly predicting what phase of precipitation ends up at the ground remains a challenging task. Besides this, many researches on the formation mechanism of microphysical processes are difficult to be applied to the precipitation type prediction, so it still needs continuous efforts to apply these achievements to improve the precipitation type prediction skill of numerical prediction model and increase the accuracy of precipitation type prediction by artificial intelligence and other technologies.