Seasonal snow is a major factor in the global climate system, which makes snow cover an important variable in climate models. Naturally, long historical observations of snow variables such as snow cover and snow depth are desired for model validation and for the interpretation of climate projections. In support of this goal, recent efforts to increase the confidence in historical datasets have been underway. We have explored the discrepancies between reanalysis snow datasets using an offline snow model, exploring first how inter-dataset temperature and precipitation biases affect seasonal snow evolution and snow spatial patterns. The method also reveals specific artifacts related to snow data assimilation in the ERA5 and JRA55 reanalysis datasets. Recently, we have applied a similar comparison framework to a set of models from the latest phase of the Coupled Model Intercomparison Project (CMIP6). Preliminary results indicate that the dominant control on hemispheric-scale snow loss the net warming, leading to a consistent sensitivity across CMIP6 model data and offline snow model data. However, biases over the historical climate period do exist, and the magnitude of projected changes on a regional scale can be highly sensitive to them. New tools should be developed to account or correct for these biases.