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Bias correction of climate variables is a standard practice in\udclimate change impact (CCI) studies. Various methodologies have been\uddeveloped within the framework of quantile mapping. However, it is well known\udthat quantile mapping may significantly modify the long-term statistics due\udto the time dependency of the temperature bias. Here, a method to overcome\udthis issue without compromising the day-to-day correction statistics is\udpresented. The methodology separates the modeled temperature signal into a\udnormalized and a residual component relative to the modeled reference\udperiod climatology, in order to adjust the biases only for the former and\udpreserve the signal of the later. The results show that this method allows\udfor the preservation of the originally modeled long-term signal in the mean,\udthe standard deviation and higher and lower percentiles of temperature. To\udillustrate the improvements, the methodology is tested on daily time series\udobtained from five Euro CORDEX regional climate models (RCMs)

A method to preserve trends in quantile mapping bias correction of climate modeled temperature