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Statistical downscaling of general circulation models (GCMs) and limited area models (LAMs) has been promoted as a method for simulating regional- to point-scale precipitation under changed climate conditions. However, several studies have shown that downscaled precipitation is either insensitive to changes in climatic forcing, or inconsistent with the broad-scale changes indicated by the host GCM(s). This has been recently attributed to the omission of the effect that changes in atmospheric moisture content have on precipitation. We describe validation of a nonhomogeneous hid- den Markov model (NHMM) for changed climate conditions and apply it to a network of 30 daily pre- cipitation stations in southwestern Australia. NHMMs fitted to 1 〈 CO2 LAM data were validated by assessing their performance in predicting 2 〈 CO2 LAM precipitation. The inclusion of 850 hPa dew point temperature depression, a predictor reflecting relative (rather than absolute) atmospheric mois- ture content, was found to be crucial to successful performance of the NHMM under 2 〈 CO2 condi- tions. The NHMM validated for the LAM data was fitted to the historical 30 station network and then used to downscale the 2 〈 CO2 LAM atmospheric data, producing plausible predictions of station pre- cipitation under 2 〈 CO2 conditions. Our results highlight that the validation of a statistical downscal- ing technique for present day conditions does not necessarily imply legitimacy for changed climate conditions. Thus statistical downscaling studies that have not attempted to determine the plausibility of their predictions for the changed climate conditions should be viewed with caution.

Validation of downscaling models for changed climate conditions: case study of southwestern Australia