Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation

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Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables (2017, methodology): “If trends are to be preserved, univariate methods such as equidistant/equiratio quantile matching and quantile delta mapping algorithms have been proposed (Li et al. 2010; Wang and Chen 2014; Cannon et al. 2015).”
Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes (2016, methodology): “More recently, the standard non-parametric QM method has been adapted to more explicitly preserve the raw modeled climate change signals (Michelangeli et al., 2009; Olsson et al., 2009; Willems and Vrac, 2011; Sunyer et al., 2015; Wang and Chen, 2014; Cannon et al., 2015).”
Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? (2015, unknown): “In appendix A, we showed that the quantile delta/perturbation, QDM, and equidistant/equiratio CDF matching (Li et al. 2010; Wang and Chen 2014) methods are all fundamentally similar.”