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Commonly used bias correction methods such as quantile\udmapping (QM) assume the function of error correction values between modeled\udand observed distributions are stationary or time invariant. This article\udfinds that this function of the error correction values cannot be assumed to\udbe stationary. As a result, QM lacks justification to inflate/deflate\udvarious moments of the climate change signal. Previous adaptations of QM,\udmost notably quantile delta mapping (QDM), have been developed that do not\udrely on this assumption of stationarity. Here, we outline a methodology\udcalled scaled distribution mapping (SDM), which is conceptually similar to\udQDM, but more explicitly accounts for the frequency of rain days and the\udlikelihood of individual events. The SDM method is found to outperform QM,\udQDM, and detrended QM in its ability to better preserve raw climate model\udprojected changes to meteorological variables such as temperature and\udprecipitation

Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes