Understanding the dynamics of 2024 extreme heat event in India: Spatial variability, hydrometeorological impacts, and model evaluation

Heatwaves are becoming more intense, frequent, and prolonged due to global warming, posing significant risks to ecosystems and human societies. Despite their profound impact, detailed regional assessments of extreme heat events remain limited, particularly in India. This study addresses the gap by systematically investigating the 2024 extreme heat event in India. We evaluated the performance of various land surface schemes in simulating heat extremes using the Weather Research and Forecasting model and also assessed the accuracy of Global Forecast System (GFS) forecasts. Our analysis reveals a strong co-occurrence of drought and heat stress during the extreme heat event. This combination results in increased fire risk and negative impacts on vegetation productivity in regions affected by both drought and heat stress highlighting the severe consequences of this compound event. We compare different land surface models (RUC, Noah, Noah-MP, Noah-MP with dynamic vegetation, CLM) against India Meteorological Department (IMD) observations. We observe that Noah is optimal for reducing bias and RMSE, while Noah-MP with dynamic vegetation is most accurate for simulating extreme heat, with the highest hit rate and threat score for the 90th percentile threshold. Additionally, GFS maximum temperature forecasts for 1–3 day lead times perform well at short lead times, especially in Southern India but overestimate temperatures in heatwave-prone regions like the Indo-Gangetic Plains. Our findings highlight the importance of enhancing land surface models and forecasting systems to better predict extreme heat events, which is crucial for localized hazard and risk assessments and improving disaster management efficiency. • Drought and heat stress increase fire susceptibility and vegetation loss. • Maximum temperature in WRF simulations is highly sensitive to land surface schemes. • Noah shows low bias and RMSE, but Noah-MP dveg better captures extreme heat events. • GFS performs well at 1-day lead forecasts, but its performance declines afterwards.