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It is essential to assess the adaptation of reservoir operation to climate change in arid regions. The main objective of this research is to propose a framework for assessment of reservoir rule-curve (RRC) adaptation for climate change scenarios. The framework is applied to an arid zone in Iran and consists of the three models: downscaling, rainfall-runoff and reservoir optimisation models. LARS-WG is tested in 99% confidence level before to using it as downscaling model. Seven artificial neural network models are proposed, examined and compared with IHACRES to find proper rainfall-runoff model for arid zone. Current and adapted reservoir rule curves are derived by dynamic programming optimisation. The results demonstrate capability of proposed framework in assessment of adaptation and show that global warming negatively influences proposed index (water supply index) in normal and wet years, but has positive influence for dry years. It also improves reservoir reliability, but it cannot restore current reliability.