Comparative Analysis of GCM Selection Approaches for Climate Change Impact Assessment in India

Reliable climate projections are crucial for informed decision-making in water resource planning and management. However, selecting suitable Global Climate Models (GCMs) remains challenging due to inherent uncertainties and computational constraints. This study introduces a novel hybrid approach for GCM selection, focusing on models that exhibit consistency in projecting future climate changes and skill in representing current climate conditions, including average climate, seasonal patterns, and climatic variations. GCM performance in simulating these critical properties was evaluated for rainfall, maximum temperature, and minimum temperature using the Kling-Gupta Efficiency (KGE) metric, resulting in a structured 3×3 performance matrix for each GCM. The matrix distances, quantifying the disparities between each GCM's performance matrix and the ideal reference matrix, were used to represent overall model performance. GCMs were then ranked based on these differences using the Jenks natural breaks classification method to identify the top-performing models for ensemble construction. The proposed method was tested by selecting GCMs for Nigeria from 19 CMIP6 GCMs. Results indicate that 15 GCMs consistently projected future climate within a 95% confidence interval. Further evaluation reveals that ACCESS.ESM1.5, BCC.CSM2.MR, CMCC.ESM2, and MRI.ESM2.0 are the most suitable for simulating Nigeria's climate. The multi-model ensemble means of the selected GCMs projected a notable increase in rainfall by 10 to 40% over most of the country and maximum and minimum temperatures by 1.0 to 3.5°C and 0.5 to 4.0°C, respectively. The proposed approach offers an effective tool for GCM selection to enhance climate projection reliability.