The land-atmosphere interaction processes are crucial for the atmospheric modelling studies as it influences the interchange of energy and matter between the land surface and atmosphere and can impact climate and weather patterns on regional and global scales. The feedback between the soil moisture (SM) and precipitation (PR) is the key factor for land surface and atmosphere interactions; however the above feedback is not well understood. The present study employs Event Coincidence Analysis (ECA) method for investigating the influence of SM on extreme PR over the Indian domain. In this study, 21 years (2000-2020) of Global Land Evaporation Amsterdam Model (GLEAM) surface SM data and root zone ( \(R_z\) ) SM data together with India Meteorological Department PR data are considered. The findings indicate that West central India (WCI) region has a higher long term relationship between SM and PR over the surface as well as \(R_z\) , than the rest of the Indian regions. The higher long term relationship between SM and PR over WCI region is associated since the WCI region is a transition region, where the land-atmosphere interaction is more pronounced as compared to either the wet or the dry regions. The results of the ECA method also shows that the number of grid points having higher trigger coincidence rate (TCR) for the highest time lag (30 days), is lower for \(R_z\) SM as compared to the surface SM. Additionally, seasonal TCR analyses are performed, using the 21 years (2000-2020) data. The results of the seasonal TCR analysis indicate that the monsoon season (June to September) shows reduced TCR magnitudes as compared to annual analyses; however the TCR results during monsoon provides similar spatial distributions as to the results of the annual analysis. The results of TCR monsoon season shows higher TCR values over in Northwest and WCI regions. Furthermore, the study employed the PCMCI causality test to examine the dynamic causal relationships between surface SM and PR over different time lags. The results of the PCMCI test shows strong short-term causal links between SM and PR in South Peninsular India, especially for lags of 1 and 2 days, while showing weaker long-term relationship between SM and PR over Northeast region. The strongest long-term causal relationship between SM and PR is observed in the Northwest India (NWI) region, with a time lag of 24 days. The above is attributed to the fact that the NWI region experiences very little impact from synoptic level weather systems that form over India. Furthermore, additionally, the PCMCI analysis reveals that the long-term causal relationships between SM and PR are significant over WCI and Central Northeast India. The results of this study demonstrate that both the ECA and PCMCI methods are effective in capturing the complex relationships between extreme SM and PR events across the Indian domain, and hence provide for deeper insights into land-atmosphere feedback mechanisms.
