The typically sparse distribution of weather stations in mountainous terrain inadequately resolves temperature variability. Accordingly, high-resolution gridding of climate data (for applications such as hydrological modeling) often relies on assumptions such as a constant surface temperature lapse rate (i.e., decrease of surface temperature with altitude) of 6.5°C km−1. Using an example of the Cascade Mountains, we describe the temporal and spatial variability of the surface temperature lapse rate, combining data from: (1) COOP stations, (2) nearby radiosonde launches, (3) a temporary dense network of sensors, (4) forecasts from the MM5 regional model, and (5) PRISM geo-statistical analyses. On the windward side of the range, the various data sources reveal annual mean lapse rates of 3.9–5.2°C km−1, substantially smaller than the often-assumed 6.5°C km−1. The data sets show similar seasonal and diurnal variability, with lapse rates smallest (2.5–3.5°C km−1) in late-summer minimum temperatures, and largest (6.5–7.5°C km−1) in spring maximum temperatures. Geographic (windward versus lee side) differences in lapse rates are found to be substantial. Using a simple runoff model, we show the appreciable implications of these results for hydrological modeling.