Recurrence Analysis of Communities from Precipitation Patterns over the Indian Peninsula

Precipitation is a crucial component of the hydrological cycle and is essential for water supply, ecosystems, and climate dynamics. 
In this context, the monsoon-driven rainfall patterns of the Indian Peninsular (IP) region exhibit a distinct spatio-temporal variability due to its strong seasonality, complex topography, and regional heterogeneity. Consequently, a key challenge is to identify local rainfall regimes and assess how their temporal evolution and recurrence patterns vary across space and seasons.
To address this, we leverage the recurrence analysis framework to comprehend the non-linear rainfall dynamics in terms of their deterministic behavior. 
We apply singular value decomposition and agglomerative hierarchical clustering to extract spatial communities with similar recurrence characteristics. In addition, Laplacian centrality is used to determine central hubs within each community. Furthermore, to analyze long-term trends in predictability and  persistence of rainfall dynamics, we employ a bootstrapping framework based on recurrence quantification analysis measures, comparing periods before and after 1991. 
The study uncovers four communities within the IP region, which are generally shifting towards less predictable dynamics over time. The overall cluster organization varies substantially in terms of season and time period, while the spatial locations of the central hubs within each community remain stable. In particular, the community located in the western region exhibits a pronounced decline in recurrence.
In summary, these findings indicate that rainfall dynamics in the IP region is undergoing both a temporal shift towards reduced predictability and a spatial reorganization. The study exemplifies the applicability of recurrence analysis to characterize the intrinsic nonlinear dynamics of the climate system, detect regime transitions, and provide insights that support disaster preparedness and adaptation planning.