Snow cover over Tibetan Plateau (TP) is not only a key land forcing for the regional and global climate but also an important water resource for surround regions. However, state-of-the-art climate models still exhibit substantial biases in simulating winter snow cover over the TP, which constitutes one of the major sources of uncertainty in climate prediction. Using satellite-based snow cover datasets, this study reveals that the Community Land Model version 5 (CLM5) systematically overestimates the winter snow cover fraction (SCF) over the TP. This bias mainly arises because the original SCF parameterization scheme neglects the spatially varying probability distribution of snowfall accumulation and underestimates snow depletion over barren land during the melting period. By accounting for the effects of non-growing-season low vegetation (i.e., withered grass stems) and topographic relief, we parameterize the snow accumulation probability factor (<em>k</em><sub>accum</sub>) instead of prescribing it as a constant. In addition, a revised factor is introduced to modify the snow depletion curve shape parameter (<em>N</em><sub>melt</sub>), thereby optimizing the SCF parameterization scheme. Preliminary validation indicates that the optimized scheme substantially reduces positive winter SCF biases over barren land and grassland by 34 %~88 %, and improves surface albedo simulations, thereby alleviating cold surface temperature biases by approximately 1~2 °C in snow-affected regions.