I wasn’t at the workshop, so I don’t know the practical examples you mention, so perhaps Ronald can address that. However, I’m a bit confused on whether you are discussing bias correction or downscaling? Bias correction would be to remove bias from a modeled timeseries, where a similar reference data exists. Statistical downscaling can use similar methods as used for bias correction, but then has the purpose of adjusting the statistical properties to mimic a higher resolution.
In SWICCA, the use is primarily of indicators, such as the average change in a variable. Thus, there are mostly no modeled timeseries that would require bias correction. Note also that the precipitation and temperature data used to force the hydrological models were already bias corrected within the IMPACT2C project.
Taking a local timeseries and scaling it with a climate change signal from the indicators can be considered a sort of downscaling. Is that what is discussed here? That could be done using e.g. the change in the mean, or the change in different moments, or percentiles of a distribution. Can you please clarify what you want to do? Perhaps with a practical example?
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