Raja Fara Raja Abd JalilKai Lun Chong0000-0001-6565-947XYuk Feng HuangMarlinda Binti Abdul MalekMohamed ElkollalyMohsen SherifAhmed El-ShafieAli Najah Ahmed2026-10-152026-10-152026-0110.1016/j.rser.2025.116364https://dspace-cris.utar.edu.my/handle/123456789/11751This review paper provides a comprehensive assessment of the methodologies, models, limitations, and recommendations for assessing the impact of climate change on global hydropower systems. It draws on numerous real-world examples and highlights crucial challenges such as data scarcity, which is exemplified by the limited historical hydrological and meteorological data in many regions. This scarcity of data impacts predictive models and simulations. The review also discusses model uncertainties and emphasizes the need for sophisticated hydrological and climate models. These models should be tested and validated against observed data to ensure reliability and accuracy. To address these challenges, the review advocates for enhanced data collection methods, advanced monitoring systems, and satellite technologies, as demonstrated by real-world examples from regions facing climate-induced risks. It also recommends advanced model sophistication, incorporating complex hydrological processes, climate drivers, and system interactions. This can be achieved through ensemble modeling approaches and machine learning algorithms to capture nonlinear relationships accurately. These advanced techniques enhance the predictive capabilities of models, leading to more robust and actionable strategies for managing hydropower resources under changing climate conditions. Overall, the review aims to improve the accuracy, reliability, and contextual relevance of climate change impact assessments on hydropower. This information can help decision-makers and practitioners to make informed decisions and implement resilient management practices for the essential water-energy nexus faced with a changing climate. © 2025 The Author(s)en-USClimate changeClimate modelHydrological modelHydropowerHydropower modelModel performanceClimate modelsDecision makingHydroelectric powerHydrologyLearning algorithmsLearning systemsMachine learningUncertainty analysisChanging climateClimate change impact assessmentsHydrological modelsHydropower systemModeling performanceReal-worldReview papersSystem methodologiestext::journal::journal article::review article