A broad range of statistical downscaling techniques has been developed in the past few years. Users of GCM-based climate information may choose from a large variety of methods conditional upon their needs. Weather generators provide realistic sequences of high temporal resolution events. With transfer functions, statistics of regional and local climate, such as conditional means or quantiles, may consistently be derived from GCM generated data. Techniques based on weather typing serve both purposes, but are less adapted to specific applications.
Downscaling means post-processing GCM data; it cannot account for insufficiencies in the driving GCM. As statistical techniques combine the existing empirical knowledge, statistical downscaling can describe only those links that have been observed in the past. Thus, it is based on the assumption that presently found links will prevail under different climate conditions. It may be, in particular, that under present conditions some predictors appear less relevant, but become significant in describing climate change. It is recommended to test statistical downscaling methods by comparing their estimates with high resolution dynamical model simulations. The advent of decades-long atmospheric reanalyses has offered the community many more atmospheric large-scale variables to incorporate as predictors.
Statistical downscaling requires the availability of long and homogeneous data series spanning the range of observed variance, while the computational resources needed are small. Therefore, statistical downscaling techniques are suitable tools for scientific communities without access to supercomputers and with little experience in process-based climate modelling. Furthermore, statistical techniques may relate directly GCM-derived data to impact relevant variables, such as ecological variables or ocean wave heights, which are not simulated by contemporary climate models.
It is concluded that statistical downscaling techniques are a viable complement to process-based dynamical modelling in many cases, and will remain so in the future.
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