Bias correction of global and regional simulated daily precipitation and surface mean temperature over Southeast Asia using quantile mapping method

A trend preserving quantile mapping (QM) method was applied to adjust the biases of the global and regional climate models (GCM and RCMs) simulated daily precipitation and surface mean temperature over Southeast Asia regions based on APHRODITE dataset. Output from four different RCMs as well as their driving GCM in CORDEX-EA archive were corrected to examine the added value of RCMs dynamical downscaling in the context of bias adjustment. The result shows that the RCM biases are comparable to that of the GCM biases. In some instances, RCMs amplified the GCM biases. Generally, QM method substantially improves the biases for both precipitation and temperature. However, the bias adjustment method works better for surface mean temperature and less so for daily precipitation. The large inter-models variability is reduced remarkably after bias adjustment. Overall, study indicates no strong evident that RCMs downscaling as an immediate step before bias correction provides additional improvement to the sub-regional climate compared to the correction directly carried out on their forcing GCM.