The authors investigate the sampling error of space-time-averaged rain rates due to temporal sampling by satellite, based on 5 years of radar-based rainfall estimates over the Mississippi River Basin, and use the data to estimate the sampling uncertainty. The two approaches used in this estimation consist of a parametric approach that ignores the diurnal cycle in the rain rate statistics and a nonparametric approach that accounts for it. Results show that the parametric approach yields uncertainty estimates that are generally smaller than those obtained by the nonparametric approach. At a sampling interval of 12 hours, the parametric approach typically underestimates the sampling uncertainty for monthly rainfall by about 28%, 25%, and 14% at averaging areas of 512 ¿ 512 km2, 256 ¿ 256 km2, and 32 ¿ 32 km2, respectively. Results verify the power law scaling characteristics of the sampling uncertainty with respect to the space-time scales of measurement and the large-scale precipitation observables suggested by simple models of rain statistics and revealed in previous studies with smaller data sets. With respect to the spatial scale and the mean rain rate, the sampling uncertainty behaves significantly differently from the inverse square-root behavior predicted by many models. The authors compare the resulting scaling exponents to those obtained from previous studies and identify the factors that need to be addressed in sampling uncertainty comparisons. The main new finding is that the power law exponent governing the dependence of the uncertainty estimate on the mean rain rate appears to exhibit seasonal variations.