Abstract: Under the influence of the stable snowstorm circulation situation of the typical double-resistance type warm area in northern Xinjiang, an extremely heavy snowfall event occurred in Altay, from January 6 to 12, 2024. Several meteorological stations broke the historical maximum records for the same period, resulting in avalanches in several places of the Altay Mountains. This event can be divided into three stages according to the evolution of circulation situation and snowfall characteristics. In this study, the snowfall characteristics, circulation situation, extreme causes, and model forecasting performance of the event were analyzed using the hourly observation data of national and regional meteorological stations, ERA5 reanalysis data, and CMA-GFS, CMA-MESO, CMA-TYM, ECMWF model forecast data. The results are as follows: (1) The event was characterized by several extreme features, such as long duration, large accumulated precipitation, and large hourly precipitation. The precipitation distribution was increasing from the plain areas to the mountain areas. The strong snowfall process mainly concentrated in the second and third stages. In these two stages, the increase in snow accumulation, large hourly snowfall, and high snow-to-water ratio in the mountainous areas along the northern Altay provided key meteorological conditions for the avalanches. (2) During this event, a strong polar front at 500 hPa continuously split short-wave systems, which successively moved eastward to affect Altay. The warm shear line at 850-700 hPa was maintained for a long time, accompanied by strong warm front frontogenesis. Heavy snowfall occurred in the divergence area at the exit of the polar front jet at 300 hPa, from the north of the shear line at 850 hPa to the regions near the shear line at 700 hPa. (3) In the second and third stages, the water vapor conditions in the north of Altay, especially in the northern mountains, were abnormally strong. With the effect of terrain amplification, the low-level dynamic uplift was exceptionally strong and maintained for a long time. These were the important reasons for the extremely heavy snowfall. (4) The regional model performed better than the global model in predicting heavy snowfall, but the capability to forecast extreme snowfall was still limited. ECMWF Extreme Forecast Index (EFI) forecast product and the Ensemble Average Abnormal Weather Forecast (EMAF) product were better at identifying extreme weather signals at medium-range lead time. It is suggested to strengthen the application of related products and make adjustments according to the quantitative forecast results of the model to further improve the precision service effect of forecasting and warning of extreme weather events.