Lapse rate-adjusted bias correction for CMIP6 GCM precipitation data: An application to the Monsoon Asia Region

Area of Science:

• Climate Science
• Atmospheric Science
• Hydrology

Background:

• Bias correction (BC) is crucial for using General Circulation Model (GCM) outputs in regional climate impact studies.
• Existing methods like Linear Scaling (LS) and Quantile Mapping (QMap) have limitations in accurately representing regional precipitation patterns.

Purpose of the Study:

• To introduce and evaluate a novel bias correction method, Local Lapse Rate Regression (LR-Reg), for GCM precipitation data.
• To compare the performance of LR-Reg against established BC methods (LS, QMap) and NASA's NEX data over the Monsoon Asia region.
• To project future precipitation changes under different Shared Socioeconomic Pathways (SSPs) and analyze shifts in precipitation patterns.

Main Methods:

• Applied LR-Reg, which incorporates local lapse rate adjustments and linear regression, to MIROC6 GCM precipitation data (CMIP6).
• Utilized historical and projected (SSP245, SSP585) datasets for the Monsoon Asia region.
• Quantitatively assessed BC method performance using Mean Absolute Error (MAE) and analyzed latitudinal precipitation shifts.

Main Results:

• LR-Reg demonstrated significant error reduction: 10-30% over LS-BC, 30-50% over QMap-BC, and 75-100% over NASA NEX-data.
• Projected dry seasons show up to 100% precipitation decrease in South Asia; wet seasons show up to 50% increase in Northeast China and Himalayan regions.
• Baseline precipitation peaks at 0 and 25 degrees latitude, shifting inwards to 10 and 20 degrees under future SSP scenarios.

Conclusions:

• The LR-Reg method offers a substantial improvement in bias correction accuracy for GCM precipitation data.
• Future climate projections indicate significant regional precipitation alterations and a notable poleward shift in monsoon precipitation zones.
• These findings are critical for refining regional climate impact assessments and adaptation strategies in Monsoon Asia.