A Comparison of Methodologies for Studying Heavy Precipitation Events during the Summer Monsoon Season in India Using Complex Network Approaches

Extreme rainfall events during the Indian monsoon season pose significant challenges due to their socioeconomic and environmental impacts. Understanding the spatial and temporal dynamics of these events requires robust analytical and statistical methods capable of capturing complex relationships within rainfall generating systems. Complex network approaches have emerged as powerful tools for analyzing spatiotemporal patterns in climate data, offering new insights into extreme weather phenomena.

This study compares two methodologies for constructing and analyzing climate networks to study the spatiotemporal structure and dynamics of heavy precipitation events in India during the monsoon season across multiple time scales. Specifically, we introduce a novel combination of Discrete Wavelet Decomposition with Event Coincidence Analysis (ECA), referred to as Multi-Scale Event Coincidence Analysis (MSECA) and compare the results with the existing Multi-Scale Event Synchronisation (MSES). From a conceptual perspective, MSECA appears to be a more reasonable method compared to MSES, as it mitigates certain undesired effects of temporal clustering of rainfall extremes across various timescales.

Our results reveal distinct differences in network properties depending on the methodology used, highlighting the sensitivity of network-based analyses to the choice of construction technique. These differences affect the identification of dominant heavy rainfall patterns and their underlying drivers, such as large-scale atmospheric circulation and/or local feedback mechanisms at daily to monthly temporal scales.

Our work underscores the importance of methodological rigor and the potential of complex network approaches in advancing the understanding of extreme rainfall events in monsoon-dominated regions. This comparison provides a foundation for developing standardized practices for network-based climate studies, enabling more robust assessments of extreme weather phenomena.