Evaluation and Ranking of CMIP6 GCMs for Temperature Extremes in the Semi-Arid Region of NorthWestern India

Climate change has intensified extreme heat events, particularly in semi-arid regions where agriculture, water resources, and livelihoods are highly climate-sensitive. The reliable temperature projections are therefore essential for effective climate risk assessment and adaptation planning. In this context, General Circulation Models (GCMs) are widely used to project future climate, but their performance varies across regions and across different temperature characteristics. This study proposes a robust framework to identify the most reliable CMIP6 GCMs for temperature projections in semi-arid regions. The proposed methodology evaluates GCM performance against observed data for both mean temperature and extreme temperature indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), categorised into intensity, duration, and frequency indices. Ten statistical evaluation metrics are used to quantify GCM performance. To minimise subjectivity and dependence on individual metrics, a leave-one-out approach is applied to determine the optimal combination of evaluation metrics. The GCMs are then ranked using two multi-criteria decision-making methods, namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Višekriterijumsko Kompromisno Rangiranje (VIKOR), to enhance the robustness of model selection. The framework is applied to the semi-arid region of North-Western India using CMIP6 GCMs for the historical period, with ERA5 used as the reference observational dataset. The results indicate that GISS-E2-1-G, KACE-1-0-G, EC-EARTH3, UKESM1-0-LL, and NORESM2-MM consistently demonstrate better performance across the region based on the optimal set of evaluation metrics (RMSE, MAE, NSE, and R²). However, no single model performs best across all temperature characteristics because of regional heterogeneity. The proposed framework provides a transparent and reproducible approach for robust GCM selection and improves the assessment of temperature extremes in semi-arid regions.

Keywords: Temperature extremes; Semi-arid region; CMIP6; TOPSIS; VIKOR