International Journal of Environment and Climate Change

Accurate representation of precipitation patterns is crucial for understanding and adapting to these impacts. General Circulation Models (GCMs) are essential for projecting future climate scenarios but often exhibit biases in simulating precipitation, undermining the reliability of their outputs. This study focused on bias correction of monthly precipitation data from different GCMs using Cumulative Density Functions (CDFs). Bias correction techniques were employed to align model-simulated precipitation with observed data, revealing significant improvements in the accuracy of future precipitation projections. The study area, Raipur, characterized by diverse topography, served as the location for analysis. Three GCMs were selected based on their availability and participation in the CMIP6 experiment. The bias correction process involved the calculation of CDFs and equiprobability transformations, resulting in a closer match between model predictions and observations. Results showed substantial variability in monthly precipitation values across different climate models and scenarios, with distinct seasonal patterns observed. Inter-model discrepancies underscored the complexities of precipitation simulations, highlighting the need for careful interpretation of model outputs. Continued research efforts were crucial for improving the accuracy and reliability of climate model simulations for informed decision-making and planning in climate-sensitive sectors.