Coupled Influence of Aerosols, Convection and Cloud Microphysics on Lightning Across Diverse Climatic Regions of India

ABSTRACT Lightning serves as a sensitive indicator of deep convection and cloud microphysics, yet its response to aerosol variations remains uncertain over climatologically diverse regions. We investigate the coupled influence of aerosols, convective instability and cloud microphysical processes on lightning activity across six regions of India using long‐term multi‐satellite, reanalysis and radiosonde datasets (2001–2023). Results show strong spatial variability, with lightning flash rate density (LFRD) maximised over the northwest and northeast India and suppressed over central and arid western regions. Seasonal analysis reveals pre‐monsoon and monsoon peaks, largely following convective available potential energy (CAPE), with minor regional offsets. Aerosol optical depth (AOD) exhibits a non‐linear relationship with lightning. Moderate AOD enhances lightning under high CAPE (> 1000 J/kg), whereas excessive loading (> 0.6) combined with very high CAPE (~1500–2000 J/kg) suppresses activity. Importantly, these thresholds vary regionally, associated with varying aerosol–lightning coupling. Aerosol type further modulates this behaviour: biomass‐burning and urban aerosols invigorate lightning by enhancing cloud liquid and ice water content in the mixed‐phase region, while dust and black carbon suppress lightning via radiative warming and stabilisation. Radiosonde and ERA5 profiles confirm mid‐tropospheric heating, increased stability and reduced vertical velocity under high AOD in dust and black carbon‐dominated regions. This study provides the first integrated, long‐term evidence over India that aerosol impacts on lightning are non‐linear and region‐dependent, governed by CAPE thresholds, aerosol type and thermodynamic adjustments. The findings underscore the competing roles of microphysical invigoration and radiative suppression in shaping thunderstorm electrification, offering new insights for improving lightning parameterizations in weather and climate models.