Evaluation of CMIP6 Climate Models and Projected IDF Changes under Climate Change: A Case Study from Monsoon-Dominated Goa, India

Effective climate predictive models are crucial for meaningful climate change mitigation, and ongoing advances in data and scientific understanding are helping to improve their accuracy. Recent advances in CMIP6 global climate models (GCMs) offer improved simulation of regional climate variables essential for hydrological design. This case study evaluates the performance of 24 CMIP6 GCMs in replicating historical precipitation, temperature, and solar radiation over Goa, a monsoon-dominated coastal region of India. Using a multimetric framework based on Compromise Programming and performance indicators (R2, percentage bias, normalized root mean square error, and Nash–Sutcliffe efficiency), CESM2 and CanESM5 were identified as the most reliable models for simulating precipitation and temperature-related variables, respectively. These top-ranked models were subsequently used to develop intensity–duration–frequency curves for 1-day, 3-day, and 5-day durations under historical (1985–2014) and future (2021–2100) periods following the shared socioeconomic pathway 4.5 (SSP4.5) scenario. The results indicate a robust and duration-dependent intensification of extreme rainfall events, with projected increases reaching up to 30% for 5-day rainfall associated with a 100-year return period. These projected changes have direct implications for urban flood management, landslide-prone areas, and reservoir safety across Goa. The case study also identifies and discusses uncertainties arising from model structure, downscaling techniques, and return period extrapolation, emphasizing the importance of using duration-specific trends and multimodel comparisons. By integrating GCM ranking with hydrologically relevant extremes, this case study offers a replicable and policy relevant framework to climate-resilient infrastructure planning in monsoon-affected regions.