CORRESPONDENCE Comment on ''Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue''

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Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool (2018, background): “…and Seibert 2012), i.e., biases are not related to the (mis)representation of physical processes in the models, such as synoptic circulation (Addor et al. 2016), and changes in trends or spatio-temporal structures may appear due to the scale mismatch between RCM and observations (Maraun 2013).”
Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts? (2018, methodology): “As a consequence, BC methods can arbitrarily modify the temporal structure of the raw model output, with the overall result of degrading the skill (Maraun 2013).”
Does applying quantile mapping to subsamples improve the bias correction of daily precipitation? (2018, methodology): “…mapping (QM) is a well performing (Themeßl et al., 2011; Gudmundsson et al., 2012; Teutschbein and Seibert, 2012; Räty et al., 2014; Fang et al., 2015; Teng et al., 2015) and widely used (Gudmundsson et al., 2012; Maraun, 2013; Wilcke et al., 2013) method for correcting daily precipitation data.”