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Energy transmission networks represent the backbone of modern societal functioning. With a changing climate system, the resilience of this critical infrastructure has become a paramount concern for grid operators in response to extreme weather events. However, assessing the systemic risk of such networks remains a significant challenge. Traditional climate impact and risk assessments often evaluate components in isolation, thus, failing to capture the complex, interconnected dependencies of high-voltage transmission, making it difficult for decision-makers to implement an informed, systemic process for risk disclosure, climate adaptation, and resilience strategies.Accounting for the systemic perspective of energy transmission networks, a complex network-based clustering approach, informed by climate risk and damage impact data, is applied to evaluate the exposure of interconnected transmission systems. Utilizing the Transpower open network asset dataset for New Zealand’s national transmission network to construct a graph-based data model. By computing local clustering coefficients to quantify structural meshing and redundancy, we identify distinct functional clusters and rank components according to their systemic criticality. This enables the translation of complex physical network topology and historical vulnerability into a prioritized hierarchy of grid exposure, identifying which nodes are most vital to maintaining stability during extreme weather events.The efficacy of this approach is demonstrated using a case study of New Zealand’s national transmission network. Our results showcase how neural networks can delineate high-risk clusters and identify linchpin assets that, if compromised by extreme weather events, would cause disproportionate systemic and cascading failures. By providing a spatially explicit ranking of grid criticality, this data-driven approach offers a scalable tool informing climate impact and risk assessments.The interdisciplinary research presented exemplifies the translation of climate and data science into decision-relevant information. It provides a robust methodology for assessing dynamically varying grid exposure, ultimately supporting the development of more resilient energy infrastructure and providing a template for advanced climate impact and risk understanding in interconnected systems.