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 was constructed 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 by downscaling 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 was no 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 observed precipitation, 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.