Annual Streamflow and Flood Event Simulation for Future Water Supply—A Multiple Lines of Evidence Approach

Abstract Effective water resource management under climate change is dependent on reliable modeling of future water availability, yet significant uncertainties remain due to shifting precipitation patterns and limitation in climate modeling. This study investigates a multiple‐lines‐of‐evidence approach aimed at reducing the uncertainty in projections of streamflow, specifically focusing on extreme flood events as proxies for total annual flows. Using subdaily rainfall‐runoff models calibrated for four climatically diverse Australian catchments, we compare projections from regional climate model (RCM) downscaling with a continuous precipitation generation approach conditioned on stable climatic covariates such as temperature. Our findings suggest that in wetter regions with pronounced extreme precipitation events, using flood events as proxies for total annual flows can reduce variance and bias, potentially offering more reliable inputs for water resource planning. Both continuous simulation and RCM downscaling demonstrated similar biases for rainfall when evaluated against historical observations. However, continuous simulation typically produced lower biases in modeled streamflow, and performed marginally better in representing low‐frequency climate variability; whilst also offering reduced computational demands, presenting a parsimonious alternative for climate impact assessment. Despite the improved precipitation representation by RCMs, compared to General Circulation Models (GCMs), epistemic uncertainty and sampling biases persist, limiting the confidence in projections of extreme streamflow events for water supply modeling. These results highlight the need for improved modeling of precipitation extremes under warming climates to refine and enhance the robustness of future water resource projections.