Rising risk and localized patterns of Indian Summer Monsoon rainfall extremes

Extreme precipitation during the Indian Summer Monsoon period often leads to heavy flooding, posing risks to infrastructure and human life across India. Modelling such phenomena requires developing spatio-temporal models that incorporate spatial dependence for a more realistic representation of spatial variation over a large domain. In this study, we employed a multivariate max-stable process-based model to capture the joint spatial dependence and to estimate the impact of geographic and temporal covariates on the risk associated with anomalous rainfall. Data pooling and local optimization at the cluster level help to reduce the uncertainty in the parameter estimates, whereas the incorporation of El Nin o Southern Oscillations (ENSO) enhances the predictive power of the models. The results indicate that approximately 75% of India's land area is at risk of experiencing heavy rainfall with a 50-year or more return period. The proposed max-stable process model adequately captured the nonstationary behaviour of Indian Summer Monsoon Rainfall (ISMR) extremes. A post-hoc analysis was conducted to assess the practical significance of differences in estimated parameters. This is essential for understanding the real-time variations in the extremal behaviour of ISMR across the country. In addition, the phase effect of ENSO and spatial scale (cluster/region) changes were quantified using a linear mixed-effects model. The results suggest that the impact of ENSO becomes more pronounced for longer return periods and intensification of ISMR extremes occurs due to the shift from El Nin o to La Nin a phases. Moreover, treating spatial scales as a random component in the modelling reveals that ISMR extremes are highly localized in nature. Specifically, around 80% of the variation in ISMR extremes for shorter return levels is attributed to the nested spatial variation.