To decisively confront the far-reaching impacts of climate change, it is crucial to identify regions that are vulnerable and sensitive to changes in rainfall patterns. Understanding sub-regional vulnerability can significantly contribute to alleviating disruptive events such as drought and flood that affect larger areas. To this end, the complex network of rainfall dynamics of the Lake District, Türkiye, one of the most significant regions with the largest freshwater resources in the country, was analysed. The most robust network structure was determined using transfer entropy coupled community detection algorithms, i.e., Optimal, Spinglass, Edge Betweenness, Louvain, and Leading Eigen. Three optimal distinct communities were identified with the Louvain algorithm, and vulnerability analysis was conducted considering internal and external factors such as inter- and external links, topological differences from the globally coupled network, and average communicability within communities. The relative vulnerability indices obtained for Comm-1, Comm-2, and Comm-3 are 1.259, 1.000, and 1.073, respectively, where the most vulnerable community is Comm-1. Furthermore, Comm-1 has the highest in- and cycle-clustering coefficients, where the stations are more influenced by others and have reciprocal and complex relationships. Comm-2 has the highest intercommunication and the largest structural difference from other communities, leading to more invulnerability. To analyse the nonlinear characteristics of communities leading to various degrees of vulnerability, complexity-entropy causality plane, recurrence plot, and recurrence quantification analyses were conducted. While Comm-1 exhibits a wide range of complexity-entropy values associated with diverse rainfall patterns leading to greater vulnerability, Comm-2 demonstrates greater regularity and predictability, characterised by well-defined rainfall regimes and temporal structures. The results could help identify critical nodes and vulnerable communities in a basin to build a reliable early warning system for accurate risk prediction under the impacts of climate change.