Bias correction and statistical downscaling of earth system models using quantile delta mapping (QDM) and bias correction constructed analogues with quantile mapping reordering (BCCAQ)

Earth System Models (ESM) is a model that can simulate, predict climate change that occurred in the past, present, and create climate change scenarios in the future. ESM output has not been able to represent local scale climate. Statistical Downscaling (SD) is a static downscaling process in which data on a large-scale grids in a certain period and time period is used as a basis for determining data on small-scale grids. SD results still have a sizeable bias, so we need a method that works to reduce the bias. The bias correction method used in this research is Quantile Delta Mapping (QDM) and bias correction constructed analogues with quantile mapping reordering (BCCAQ). This study downscales the rainfall and maximum temperature data generated from Beijing Normal University Earth System Model (BNU-ESM) and ERA-Interim as the proxy of the observation. The skill is verified by means of Taylor Diagram showing the correlation value, the Root Mean Square Error (RMSE), and standard deviation. Based Taylor Diagram the QDM has better performance compared to the BCCAQ method. The performance of downscaling and bias correction during September-October-November (SON) is the best compared to other seasons.