Recurrence analysis of meteorological data from climate zones in India.

This article focuses on analyzing the spatiotemporal variability of climate dynamics across 15 locations in India using temperature and relative humidity data from 1948 to 2022. Employing nonlinear time series analysis, specifically recurrence quantification analysis (RQA) and recurrence networks, the study reconstructs underlying climate dynamics to detect shifts and heterogeneity in complexity over time and space. The findings reveal significant regime shifts around 1972–79 toward more irregular and stochastic temperature dynamics, followed by a return to more regular dynamics after 2000, patterns that correlate with El Niño–Southern Oscillation (ENSO) events and Indian Summer Monsoon variability. Clustering based on recurrence measures shows spatial heterogeneity that does not strictly align with traditional Köppen–Geiger climate zones, highlighting location-specific responses influenced by atmospheric, oceanic, and local factors. The study underscores the importance of analyzing dynamical variations in meteorological data at multiple spatial scales to better understand regional climate variability in India.