Using quantile mapping and random forest for bias-correction of high-resolution reanalysis precipitation data and CMIP6 climate projections over Iran

Climate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CMCC-ESM2 and MRI-ESM2-0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias-correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2-4.5, SSP3-7.0 and SSP5-8.5). First, bias-correction was performed on ERA5-Land reanalysis data as reference period (1990–2020) using the QM method, then the corrected ERA5-Land reanalysis data was considered as measured data. Based on the corrected ERA5-Land reanalysis data (1990–2020) and historical simulations (1990–2014), the future projections (2015–2100) were also bias-corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5-Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases; hence, a combination of QM-RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC-ESM2 revealed the highest RMSE in both SSP2-4.5 and SSP3-7.0 amounting to 331.74 and 201.84 mm·year⁻¹, respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5-8.5, notably in the case of ACCESS-ESM1-5 (RMSE = 431.39 mm·year⁻¹), while the RMSE reduced after using QM-RF method (197.75 mm·year⁻¹). Obviously, a significant enhancement in results was observed upon implementing the QM-RF combination method in CMCC-ESM2 under both SSP2-4.5 (RMSE = 139.30 mm·year⁻¹) and SSP3-7.0 (RMSE = 151.43 mm·year⁻¹) showcasing approximately reduction in RMSE values by 192.43 and 50.41 mm·year⁻¹, respectively. Although each bias-corrected model output was evaluated individually, multi-model ensemble (MME) was also created to project the annual future precipitation pattern in Iran. By considering that combination of QM-RF method revealed the lower errors in correcting model outputs, we used the QM-RF technique to create the MME. Based on SSP2-4.5, the MME climate projections highlight imminent precipitation reductions (>10%) across large regions of Iran, conversely projecting increases ranging from 10% to over 20% in southern areas under SSP3-7.0. Moreover, MME projected dramatic declines under SSP5-8.5, especially impacting central, eastern, and northwest Iran. Notably, the most pronounced possibly decline patterns are projected for arid regions (central plateau) and eastern areas under SSP2-4.5, SSP3-7.0 and SSP5-8.5.