Application of Linear Scaling Post-Processing Method for Bias Correction of Climate Models Retrieved from CMIP6

Global warming changes precipitation and temperature patterns. In this study, the linear scaling post-processing method was used to correct the biases of seven climate models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6). Also, the performance of these models was evaluated for simulating temperature and precipitation in the Hamedan-Bahar plain in the baseline period of 1990 to 2014. Three performance indicators (mean square root error, Nash-Sutcliffe, coefficient of correlation) were used to quantify the models᾽ reproducibility to climatic parameters for the historical period (1990–2014). Based on the results, CMCC-ESM2, MIROC 6, and NorESM2-MM models were selected and their ensemble were used to project temperature and precipitation in near (2026–2050), mid (2051-2075) and far futures (2076-2100) under three socioeconomic shared pathway (SSP) 1–2.6, 2–4.5 and 5–8.5. Based on the obtained results, in all three periods, the average precipitation decrease in summer is higher than the other seasons, and the highest amount of decrease will occur in the mid future period under the SSP5-8.5 scenario (67%). Moreover, the minimum and maximum temperatures increased in all three future periods and under all three scenarios. The highest increase of temperature occurred in January and February. Predicting climate parameters using selected models in this study can be a basis for planning and managing water resources and developing the necessary measures to climate change adaptation in Hamadan-Bahar region.