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The objective of this paper is to identify better performing Coupled Model Intercomparison Project phase 3 (CMIP3) globalclimate models (GCMs) that reproduce grid-scale climatological statistics ofobserved precipitation and temperature for input to hydrologic simulationover global land regions. Current assessments are aimed mainly at examiningthe performance of GCMs from a climatology perspective and not from ahydrology standpoint. The performance of each GCM in reproducing theprecipitation and temperature statistics was ranked and better performingGCMs identified for later analyses. Observed global land surfaceprecipitation and temperature data were drawn from the Climatic Research Unit (CRU) 3.10 griddeddata set and re-sampled to the resolution of each GCM for comparison.Observed and GCM-based estimates of mean and standard deviation of annualprecipitation, mean annual temperature, mean monthly precipitation andtemperature and Köppen–Geiger climate type were compared. The mainmetrics for assessing GCM performance were the Nash–Sutcliffe efficiency(NSE) index and root mean square error (RMSE) between modelled and observed long-term statistics. Thisinformation combined with a literature review of the performance of theCMIP3 models identified the following better performing GCMs from ahydrologic perspective: HadCM3 (Hadley Centre for Climate Prediction andResearch), MIROCm (Model for Interdisciplinary Research on Climate) (Center for Climate System Research (The University ofTokyo), National Institute for Environmental Studies, and Frontier ResearchCenter for Global Change), MIUB (Meteorological Institute of the Universityof Bonn, Meteorological Research Institute of KMA, and Model and Datagroup), MPI (Max Planck Institute for Meteorology) and MRI (JapanMeteorological Research Institute). The future response of these GCMs wasfound to be representative of the 44 GCM ensemble members which confirmsthat the selected GCMs are reasonably representative of the range of futureGCM projections

Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation