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Precipitation fields from the National Centers for Environmental Prediction (NCEP) reanalysis are validated with high-resolution, gridded precipitation observations over Oregon and Washington. The NCEP reanalysis is thought of as a proxy for an ideal GCM that nearly perfectly represents the synoptic-scale pressure, temperature, and humidity but does not resolve the complex topography of this region. The authors’ main goal is to understand how useful precipitation fields from such a model are for estimating temporal variability in local precipitation. The gridded observations represent area-averaged precipitation on a 50-km grid and have daily temporal resolution. They are calculated with a newly developed scheme, which explicitly takes into account the effect of the topography on precipitation. This gridding method profits from the already existing, high-resolution climatologies for the monthly mean precipitation in the United States, obtained from the Precipitation–Elevation Regressions on Independent Slopes Model (PRISM), by using these climatologies for calibration. The estimation of daily precipitation on scales as small as 4 km is also discussed. The reanalysis captures well precipitation amounts and month-to-month variability on spatial scales of about 500 km or three grid cells, which indicates a good performance of the precipitation parameterization scheme. On smaller spatial scales the NCEP reanalysis has systematic biases, which can be mainly attributed to the poor representation of the topography but nevertheless can be used to reconstruct the temporal variability of local precipitation on daily to yearly timescales. This suggests that GCM precipitation might be a good predictor for statistical downscaling techniques.
Widmann et al. (Thu,) studied this question.