Snow cover plays a significant role in climate change and hydrological cycle due to its specific properties, regulating energy and water exchange for atmosphere and land surface. The quality of the forcing data has a great influence on the result of model simulation. This paper adopts CLDAS and CLDASPrcp data driving on Noah 3.6 land surface model to simulate snow variables, and assesses snow cover fraction (SCF), snow depth (SD), the snow water equivalent (SWE) in major snow areas, such as Northeast China, Xinjiang and Tibetan Plateau Region. The result shows that CLDASPrcp can improve snow simulation in the winter, removes poor snow simulation due to underestimating precipitation of CLDAS. Model result of Northeast China is the most consistent with observations, CORR of SCF, SD and SWE are 0.42, 0.78 and 0.93 respectively. The improvement of snow water equivalent is most obvious, RMSE and BIAS are reduced by 54.8% and 83.1% respectively, while CORR is increased by 0.47. Thus, CLDASPrcp not only has better simulation capability but also reflects the extreme snow enevnts.