Atmospheric moisture content is critical in hydrological modeling yet is sparsely measured in mountainous environments. We compared densely distributed measurements of dew point temperature in two study sites in the Sierra Nevada, California, against (1) simple empirical algorithms, (2) the Parameter-elevation Regressions on Independent Slopes Model (PRISM), (3) radiosonde data, and (4) the Weather Research and Forecasting (WRF) mesoscale model. Empirical algorithms that used only one sea-level measurement of dew point to extrapolate to higher elevations often did not match local dew point lapse rates and could be biased as high as 9.9°C. PRISM improved upon these methods by using local observations to determine the local average dew point lapse rate, with median bias values of −0.3°C and 3.3°C in our two study sites. Empirical algorithms that derived dew point from air temperature showed a seasonal variation in performance; summer median bias values were 0.6°C–8.2°C wetter than winter bias values. Radiosonde readings showed median biases of −6.5°C and −8.0°C from observations in our study sites. WRF improved on the radiosonde data, performing well in representing both the overall trends in the basin (with median biases of −0.9°C and −1.0°C in our study sites). One base station within the basin paired with PRISM lapse rates showed small biases from overall moisture trends. However, a physically resolved model such as WRF was better equipped to represent daily dew point variations and in basins with nonlinear trends.