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

Bias correction of climate variables is a standard practice in Climate Change Impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long term statistics due to the time dependency that the temperature bias. Here, a method to overcome this issue without compromising the day to day correction statistics is presented. The methodology separates the model temperature signal into a normalized and a residual component relatively to the molded reference period climatology, in order to adjust the biases only for the former and preserve intact the signal of the later. The results show that the adoption of this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. The methodology is tested on daily time series obtained from five Euro CORDEX RCM models, to illustrate the improvements of this method.