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Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management‐relevant aspects of the hydrograph using non‐parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer‐resolution simulations are useful. Plain Language Summary: Estimating how much snow is in the mountains is usually done with computer models that divide the landscape into square patches measuring about 500 m or greater on a side, but we know that snow depth can change substantially over smaller distances. This study investigated how changes in (a) the size of the squares (bigger squares are coarser scale, smaller squares are finer scale) representing snow in computer models and (b) the weather information used to run the computer model alters the estimates of the amount of mountain snow. We found that coarse scale computer models tended to have deeper snow and that fine scale models had longer snowmelt seasons. Fine scale computer models had better agreement with satellite observations of where snow was covering the landscape. When model estimates of snow amount were compared to streamflow records from 13 watersheds in Colorado, USA we did not find that fine scale models outperformed coarse models. These results suggest that fine scale computer models are useful to put snow in the right place and represent snowmelt in patches on the landscape but coarser models are sufficient for predicting streamflow when using statistical regression methods. Key Points: Snow simulations of April 1 snow water equivalent are similar at resolutions at or below ≈150 mFine‐resolution (30 m) snow simulations agree best with snow covered area observationsSimulations with 500 m and 1,000 m spatial resolutions show acceptable performance for interannual correlations with streamflow [ABSTRACT FROM AUTHOR]