Abstract: In order to correct the diagnostic scheme and improve the forecast of precipitation type in numerical models, based on upper layer and surface observational data, combining with ERA5 precipitation type diagnosis products (PTYPE) and temperature and dew point of different pressure levels and surface, the PTYPE diagnostic and observational sounding data for different precipitation types in the northern, southwestern and Jiangnan area of China from the winter of 2022 to 2023 are compared in order to analyze the causes of forecast deviations in the PTYPE products from the aspects of statistical conceptual models and physical processes, then the improvements are proposed. The results show that (1) the sum of dry and wet snow incidents recognized by PTYPE is nearly equal to the number of observational snow events in the northern and Jiangnan area. However, in the southwestern area, the model diagnosis tends to overestimate the number of snowfall and sleet events, which means the PTYPE product is noticeably less reliable in this area. (2) The PTYPE diagnostic scheme is not consistent with the fundamental physical process of freezing rain formation, overlooking the droplets' supercooled characteristics, which could be the primary reason for the “false alarm” of the freezing rain in the PTYPE product. (3) For ice pellets, the PTYPE scheme requires that surface temperature is less than 0 °C, which is opposite to the observation. This is the main reason for the miss alarm of ice pellets. Additionally, the diagnostic scheme only requires that the solid percent of the water-phase particles near the surface is majority (≥50%), rather than the most even the all, which leads to “false alarm”. (4) Base on comprehensive analysis, the better PTYPE diagnosis plan is listed: the surface wet-bulb temperature is added as a criterion for sleet and snow; near-surface droplet temperature is added for freezing rain; the surface temperature is removed and the threshold of "near-surface liquid water content" is appropriately lowering for ice pellets, which may improve the accuracy of these precipitation types.