AbstractThe main objective of this study is to identify the socioeconomic, meteorological, and geographical factors associated with the severity of COVID‐19 pandemic in India. The severity is measured by the cumulative severity ratio (CSR)—the ratio of the cumulative COVID‐related deaths to the deaths in a pre‐pandemic year—its first difference and COVID infection cases. We have found significant interstate heterogeneity in the pandemic development and have contrasted the trends of the COVID‐19 severities between Maharashtra, which had the largest number of COVID deaths and cases, and the other states. Drawing upon random‐effects models and Tobit models for the weekly and monthly panel data sets of 32 states/union territories, we have found that the factors associated with the COVID severity include income, gender, multi‐morbidity, urbanization, lockdown and unlock phases, weather including temperature and rainfall, and the retail price of wheat. Brief observations from a policy perspective are made toward the end.