The complex interactions between freshwater and oceanic systems make estuarine environments particularly vulnerable to climate change. Addressing these challenges requires adaptable solutions that can evolve over time, as one-time actions are insufficient for such complex systems. This study presents a novel methodology to identify optimal adaptation pathways by combining a climate emulator with an optimization algorithm, while accounting for uncertainties in current knowledge and future projections. The methodology is exemplified with the analysis of flood risk at a local factory due to fluvial and coastal hazards. The climate emulator provides synthetic time series of the forcing agents that are long enough to accurately considered all plausible combinations of the variables. A medium resolution hybrid model is employed to select annual maxima events based on a key indicator (local total water level). Representative events are then selected with a clustering algorithm to simulate them with a high-resolution model to evaluate the flood risk. By integrating long-term variability, uncertainty, and precision modeling, this approach offers a flexible and robust framework for climate adaptation planning in dynamic and vulnerable systems.