AbstractDespite the implications of meteorological drought propagation to agricultural, hydrological, and groundwater droughts, the focus of previous studies has been primarily on meteorological droughts in India. We use the well‐calibrated and evaluated Variable Infiltration Capacity‐ Simple Groundwater Model (VIC‐SIMGM) to simulate soil moisture, runoff, and groundwater storage variability in India for the 1951–2016 period. The Integrated Drought Index (IDI) that combines meteorological, agricultural, hydrological, and groundwater droughts was developed for the 1951–2016 period for India. Using a spatial clustering algorithm based on the traditional interpoint distance metric, eight homogeneous clusters based on IDI were identified. The majority of clusters in India experience the onset and termination of droughts during the summer monsoon season (June–September). The analysis of moisture back trajectories using the Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT) model showed that the Arabian Sea and Bay of Bengal are the two major moisture sources for the identified clusters in India. We performed the Empirical Orthogonal Function (EOF) and Maximum Covariance Analysis (MCA) using monthly IDI and Sea Surface Temperature (SST) to evaluate the influence of long‐term climate variability on droughts in India. Droughts based on 1‐month IDI that affect a majority of drought clusters are associated with the positive phase of El Nino Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD). On the other hand, drought clusters in the Gangetic Plain and peninsular India are affected by the SST warming over the Indian Ocean. Overall, drought clusters based on IDI, their moisture source, and large‐scale teleconnection can assist in drought management and assessment in India.