Downscaling is the tool by which global climate model outputs are translated into regionally relevant projections, ready to be used for impact assessment studies or guidance for decision making or adaptive planning. However, there are many different ways in which global climate model outputs are ‘scaled’ to finer resolutions or even point locations. Some methods are nearly as complex as the global climate models themselves, whilst others involve merely overlaying the mean change as given by the global model onto observed data. Inevitably, the type of information about climate change that a user may get from such different methods is rather varied. For example, if using downscaled data that essentially is scaled observed time series then the user should not expect the downscaled data to contain information about how sequencing of events may change in the future. Opting for datasets that have been generated by more complex methods can sometimes provide richer information, but comes with other issues. For example, it is more difficult to assess if the downscaling method itself influences the climate change signal when the dataset has been generated by a complex downscaling method; introducing a type of method bias that is difficult to quantify.

In this presentation, we discuss guidance on choice of downscaling data provided to Natural Resources Management (NRM) representatives, as part of the CSIRO and BoM led work on providing new climate projections funded by the Australian Government’s Clean Energy Future Plan. Our advice focuses on issues around abilities and limitations of different downscaling approaches and is aimed at users rather than producers of downscaled data.