Bias Correction and Trend Analysis of Temperature Data by a High-Resolution CMIP6 Model over a Tropical River Basin

Technological advancements like increase in computational power have led to high-resolution simulations of climate variables by Global Climate Models (GCMs). However, significant biases exist in GCM outputs when considered at a regional scale. Hence, bias correction has to be done before using GCM outputs for impact studies at a local/regional scale. Six bias correction methods, namely, delta change (DC) method, linear scaling (LS), empirical quantile mapping (EQM), adjusted quantile mapping (AQM), Gamma-Pareto quantile mapping (GPQM) and quantile delta mapping (QDM) were used to bias correct the high-resolution daily maximum and minimum temperature simulations by Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 (MRI-AGCM3–2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6), of Netravati basin, a tropical river basin on the south-west coast of India. The quantile-quantile (Q–Q) plots and Taylor diagrams along with performance indicators like Nash–Sutcliffe efficiency (NSE), the Root-Mean Square Error (RMSE) or Root-Mean Square Deviation (RMSD), the Mean Absolute Error (MAE), the Percentage BIAS (PBIAS) and the correlation coefficient (r) were used for the evaluation of the performance of each bias correction method in the validation period. Considerable reduction in the bias was observed for all the bias correction methods employed except for the LS method. The results of QDM method, which is a trend preserving bias correction method, was used for analysing the trend of future temperature data. The trend of historical and future temperature data revealed an increasing trend in the annual temperature. An increase of 0.051 °C and 0.046 °C is expected for maximum and minimum temperature annually during the period 2015 to 2050 as per RCP 8.5 scenario. This study demonstrates that the application of a suitable bias correction is needed before using GCM projections for climate change studies.