National-scale Rainfall Bias Correction CMIP6 Projections Using Statistical Methods with Explicit SSP Comparison and Seasonal Regimes

Abstract

General Circulation Models (GCMs) play a vital role in forecasting future climate changes; however, their rainfall outputs frequently exhibit significant biases due to coarse resolution and oversimplified processes. Consequently, it is essential to apply bias correction methods to this data before utilizing it for climate change initiatives. This study introduces a context-aware framework that integrates multiple statistical bias correction methods specifically tailored to the climatic zones and rainfall characteristics of Sri Lanka. Rainfall data from 68 monitoring stations across the island were utilized alongside outputs from the CNRM-CM6-1 GCM. The study was conducted under moderate and high climate change scenarios, Shared Socioeconomic Pathways (SSP) (SSP2-4.5 and SSP5-8.5) from the CMIP6 program. Bias correction techniques, including Linear Scaling (LS), Empirical Quantile Mapping, Delta Change, Power Transformation, and Local Intensity Scaling, were employed to evaluate the performance of each method. The framework dynamically identifies the most effective method based on metrics such as RMSE, R², and Taylor diagrams. The results indicate that LS significantly enhances rainfall accuracy, particularly for monthly precipitation, outperforming other methods under SSP2-4.5. However, LS has limitations, particularly regarding its inability to account for topographic variation and spatial biases, highlighting the necessity for complementary approaches. Spatial analysis revealed regional disparities, especially in northern areas, attributed to local climate dynamics. Furthermore, the corrected data demonstrate improved accuracy and effectively capture seasonal trends, including intensified extremes projected under SSP5-8.5. This research underscores the importance of adaptive, regionally tailored correction strategies to enhance the reliability of rainfall data in climate impact assessments and water resource management.