Unveiling Hidden Patterns in Monsoon Rainfall: A Complexity‐Entropy Analysis of Tropical Precipitation Dynamics in Northeast India—A Case Study of Agartala, Tripura (2012–2024)

ABSTRACT Understanding the intricate patterns underlying tropical monsoon rainfall remains a fundamental challenge in climate science, with profound implications for water resource management and disaster preparedness. In this study, an advanced information‐theoretic framework was employed to characterise the complexity and predictability of rainfall dynamics in Agartala, Northeast India, using a comprehensive 13‐year dataset (2012–2024) of daily meteorological observations. The complexity‐entropy causality plane (CECP)—a powerful diagnostic tool that maps systems based on their randomness (entropy) versus structural organisation (complexity)—was applied alongside multiscale analysis, seasonal regime comparisons, and precursor detection for extreme events. Results revealed that Agartala rainfall exhibits intermediate entropy () and moderate complexity (), positioning it distinctly between purely random noise and perfectly periodic signals. A striking seasonal contrast was identified: non‐monsoon periods demonstrated significantly higher complexity () despite lower rainfall totals, while active monsoon exhibited high randomness () with reduced structure ()—a paradox explained by the intermittent clustering of winter rainfall versus the frequent, semi‐independent convective events during summer monsoon. Critically, statistically significant entropy elevations () were detected in 30‐day windows preceding extreme rainfall events, suggesting that measurable changes in rainfall patterns may serve as early warning signals. Temporal analysis spanning 13 years revealed modest inter‐annual variability, with 2023 exhibiting anomalous low entropy and peak complexity corresponding to documented monsoon irregularities. These findings demonstrate that complexity‐entropy analysis provides quantitative, physically interpretable metrics for rainfall characterisation beyond traditional statistical approaches, offering new pathways for predictive frameworks and climate change impact assessment in vulnerable tropical regions.