Nonlinear amplification of vegetation drought under compound dry–hot extremes across India

Understanding how vegetation responds to concurrent drought and heat is critical under a warming climate, yet vegetation drought studies in India have largely emphasized individual climatic drivers, correlation-based relationships, or regional analyses. A national-scale assessment that quantifies how rainfall deficits and temperature extremes jointly amplify vegetation drought, and separates the relative contributions of dryness and heat, remains lacking. To address this gap, we applied a meta-Gaussian probabilistic framework across India for 2001–2024 using IMD gridded rainfall and temperature and MODIS NDVI. Conditional probabilities of vegetation drought were estimated under progressively severe rainfall (P30–P10) and temperature (T70–T90) thresholds, together with compound dry–hot conditions and dedicated amplification metrics. Results show that both rainfall deficits and temperature extremes are critical drivers, with rainfall-related vegetation drought probabilities increasing from ∼0.40–0.60 under moderate deficits to ∼0.50–0.70 under extreme deficits, while temperature-related probabilities remain around ∼0.40–0.55. Under compound dry–hot extremes, vegetation drought probability rises sharply to >0.80, demonstrating strong nonlinear amplification. Dryness-driven amplification (CD ≈ 0.6–0.8) exceeds heat-driven amplification (CH ≈ 0.3–0.6), revealing a clear asymmetry in compound effects. Central, northern, western, and peninsular India emerge as high-sensitivity regions, whereas northeastern India and the Western Ghats show relatively lower sensitivity, and the Himalaya exhibits a contrasting temperature-limited response. These findings provide a national-scale basis for incorporating compound hydroclimatic extremes into vegetation drought assessment and climate adaptation planning.