| Summary: | This paper presents a novel statistical downscaling method based on a non-linear classification technique known as self-organizing maps (SOMs) and has therefore been named SOM-SD. The relationship between large-scale atmospheric circulation and local-scale surface variable wasconstructed in a relatively simple and transparent manner. For a specific atmospheric state, an ensemble of possible values was generated for the predictand following the Monte Carlo method. Such a stochastic simulation is essential to explore the uncertainties of climate change in the future through a series of random re-sampling experiments. The novel downscaling method was evaluated bydownscaling daily precipitation over Southeast Australia. The large-scale predictors were extracted from the daily NCAR/NCEP reanalysis data, while the predictand was high resolution gridded daily observed precipitation (1958–2008) from the Australian Bureau of Meteorology. The results showed that the method works reasonably well across a variety of climatic zones in the study area. Overall, there wasno particular zone that stands out as a climatic entity where the downscaling skill in reproducing all statistical indices was consistently lower or higher across seasons than the other zones. The method displayed a high skill in reproducing not only the climatologic statistical properties of the observedprecipitation, but also the characteristics of the extreme precipitation events. Furthermore, the model was able to reproduce, to a certain extent, the inter-annual variability of precipitation characteristics.
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