Classical calibration methods in hydrology typically rely on a single cost function computed on long-term streamflow series. Even when hydrological models achieve acceptable scores in NSE and KGE, imbalances can still arise between overall model performance and its ability to simulate flood events, particularly flash floods. Enhancing multi-criteria calibration methods with multi-scale signatures to improve distributed flood modeling remains a challenge. In this study, the potential of hydrological signatures computed continuously and at the scale of flood events, are employed within various multi-criteria calibration approaches to attain a more efficient hydrological model. We present a novel sensitivity and signatures-based calibration framework, implemented in the variational data assimilation algorithm of SMASH platform, which we apply to 141 catchments mostly located in the French Mediterranean region. Our approach involves computing several signatures, including flood event signatures, using an automated flood segmentation algorithm. We select suitable signatures for constraining the model based on their global sensitivity with the input parameters. We then perform two multi-criteria calibration strategies using the selected signatures, including a single-objective optimization approach, which transforms the multi-criteria problem into a single-objective function, and a multi-objective optimization approach, which uses a simple additive weighting method to select an optimal solution from a set of non-inferior solutions. Our results show significant improvements in both calibration and temporal validation metrics, especially for flood signatures, demonstrating the robustness and delicacy of our signatures-based calibration framework for enhancing flash flood forecasting systems.