Tailored calibration of stochastic weather generators for enhanced hydrological system evaluation

• Tailored calibration of SWGs offers more flexibility than conventional methods. • Captures key climate attributes for system responses and impact assessments. • Overcomes underestimation of extreme rainfall and mean/high flows in case study. • Suitable optimizer ensures efficiency and reliable parameter identification. Stochastic weather generators (SWGs) are widely used in climate impact assessments for water resource management, flood protection, crop simulation, and renewable energy. However, conventional calibration methods often fail to accurately capture key climate attributes required for these assessments. This study (1) demonstrates the potential advantages of tailored calibration, using the Simulated Method of Moments (SMM), in better capturing important climate attributes for both historical and future climates; (2) highlights the importance of robust optimization when using SMM; and (3) shows the impact of these choices on estimating critical hydrological responses such as streamflow. The method is tested on three catchments in eastern Australia, using the WGEN rainfall generator and GR4J rainfall-runoff model. Results show that conventional calibration underestimates historic extreme multi-day rainfall by ∼ 30 % and extreme streamflow by 30–50 %, while tailored calibration with SMM effectively eliminates these biases. A climate stress test further shows that common optimization techniques can introduce substantial errors (up to 20 %) in key climate attributes, while the Robust Gauss-Newton (RGN) algorithm significantly reduces these errors and cuts uncertainty in future streamflow predictions by up to 75 % for a given change in target attributes. These findings underscore the potential of tailored calibration for improving the flexibility, accuracy, and efficiency of SWG calibration.