Compound event research has gained significant momentum over the past few years. Traditionally risk assessment studies considered either one climatic driver or process at a time. However, it is now being recognised that it is the combination of multiple drivers and their statistical dependencies that lead to aggravated, non-linear impacts. We aim to identify hotspots for SM-P coupling over India from 2004 to 2020 using Event Coincidence Analysis (ECA) and an extremal tail dependence measure. We further characterise how these complex interconnected interactions can lead to more significant flash floods and landslide risk. The analysis is done at different temporal scales to pinpoint a location prone to floods during the year. High precursor coincidence rates (>60%) were obtained for traditional flash flood-prone areas over India, indicating the robustness of the approach. ECA results were compared with the probabilistic extreme value approach, and a similar pattern was observed in both. The increase in hotspots from 2004 to 2020 matches the observed increase in flood-prone districts reported by earlier studies. We also used the trigger coincidence rate to identify areas where soil moisture anomalies can trigger extreme precipitation. The seasonal variations in precursor coincidence rates are observed to be the same as those usually expected due to changing atmospheric circulation patterns. Our results will complement the traditional flood risk assessment studies and have implications for better understanding the dynamic, ever-evolving nature of compound preconditioned flooding events worldwide.