A Statistical Downscaling Model (SDM) developed at the Bureau of Meteorology has now reached a stage where high resolution climate change projections of essential surface variables (rainfall and daily temperature), at a daily time scales, can be generated for the entire Australian continent. These projections are resolved onto on a horizontal grid of approximately 5-by-5km, and are based on an existing international database of global climate model projections used for assessment of climate change impacts as defined by the Intergovernmental Panel on Climate Change (IPCC). The statistical linkage at the core of the SDM is based on the idea of daily meteorological analogues where optimal matching synoptic weather patterns are searched for in a historical database. The historical database employed here is made up of reanalyses of atmospheric circulation as observations for the large-scale predictors, and the Bureau of Meteorology's high quality in-situ observations consisting of a sparse network of about 100 to 200 stations across the continent. A combination of daily atmospheric variables are used for regional and seasonal optimization of the SDM (e. g. mean sea level pressure combined with an upper air moisture variable to predict rainfall or an upper level measure of temperature and air flow to predict surface temperatures) resulting in a total of 120 individual statistical models that describe a wide variety of Australian climates spanning from the tropical monsoon in the north to cool and temperate in the south. The 120 models are a product of: Regionalisation of the Australian continent into 10 climate zones where a different statistical model is optimized for each region; Meteorological analogues are chosen from within the same calendar season, thus 4 different models are optimized for each calendar season; and Analogues are searched separately for the three surface predictands (Rainfall, T-max and T-min). While the optimization and original application of the SDM was based on in-situ data, here we present the application of the technique to the latest surface gridded observations produced by the Bureau of Meteorology as part of the Australian Water Availability Project (AWAP). In this communication, we present and discuss the evaluation of the results using the gridded observations, including: The ability of the technique to reproduce the mean and variance of the observed local series; The ability of the technique to reproduce day-to-day variability, inter-annual variability and long-term trends; and The ability of the technique, despite being based on a univariate approach, to reproduce the observed relationships amongst individual predictands (rainfall and temperature). Following this, each of the individual SDMs are applied to climate scenarios described by a suite of global climate models. The flexibility and low cost of the SDM makes for easy application to the large number of existing climate simulations, and to sample the uncertainties attached to the plausible future emission trajectories as well as the possible response of the climate system as modelled by the current climate models. In this regard, the SDM represents an essential ingredient for assessing future climate change uncertainties at a scale relevant for local climate impact studies. It has been noted in recent applications of downscaled climate projections presented here, that researchers can produce detailed climate change impact studies allowing for the development of well informed climate change adaptation policies.
