Abstract
Statistical downscaling techniques have been developed to address the spatial scale disparity between the horizontal computational grids of general circulation models (GCMs), typically 300–500 km, and point‐scale meteorological observations. This has been driven, predominantly, by the need to determine how enhanced greenhouse projections of future climate may impact at regional and local scales. As point‐scale precipitation is a common input to hydrological models, there is a need for techniques that reproduce the characteristics of multi‐site, daily gauge precipitation. This paper investigates the ability of the extended nonhomogeneous hidden Markov model (extended‐NHMM) to reproduce observed interannual and interdecadal precipitation variability when driven by observed and modelled atmospheric fields. Previous studies have shown that the extended‐NHMM can successfully reproduce the at‐site and intersite statistics of daily gauge precipitation, such as the frequency characteristics of wet days, dry‐ and wet‐spell length distributions, amount distributions, and intersite correlations in occurrence and amounts. Here, the extended‐NHMM, as fitted to 1978–92 observed ‘winter’ (May–October) daily precipitation and atmospheric data for 30 rain gauge sites in southwest Western Australia, is driven by atmospheric predictor sets extracted from National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis data for 1958–98 and an atmospheric GCM hindcast run forced by observed 1955–91 sea‐surface temperatures (SSTs). Downscaling from the reanalysis‐derived predictors reproduces the 1958–98 interannual and interdecadal variability of winter precipitation. Downscaling from the SST‐forced GCM hindcast only reproduces the precipitation probabilities of the recent 1978–91 period, with poor performance for earlier periods attributed to inadequacies in the forcing SST data. Copyright © 2004 John Wiley & Sons, Ltd.