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Predictability of high-impact weather constitutes a major challenge. In this study, we contribute to this relevant topic by analyzing the characteristics of multiple ensemble prediction systems aimed at sampling initial and/or model uncertainties for three exemplary heavy precipitation systems that occurred over the western Mediterranean. In particular, initial condition perturbations are generated by (i) directly downscaling the ensemble members from a global ensemble and (ii) creating perturbations by sampling a wide range of scales using an adaptation of the breeding methodology. Model error is sampled by applying stochastic perturbations to physical parameterizations and to microphysics parameters. A positive impact in terms of ensemble diversity is obtained when initial condition perturbations across a broad range of scales are applied, especially at low levels and for cases in which local factors are more relevant. Regarding stochastic model perturbations, they display very localized perturbations with low amplitude, which are insufficient to capture extreme scenarios, except when local factors play a dominant role. However, when combined with initial condition perturbations, they generally increase diversity and perturbation amplitude over areas characterized by deep moist convection. This outcome exhibits the positive impact of sampling both uncertainty sources, even in the extremely nonlinear regime that defines convective-scale phenomena. • Initial condition perturbations sampling multiple scales increase the ensemble diversity and skill obtained with downscaling. • Stochastic perturbations are insufficient to produce skillful forecasts, except when scattered convection is dominant. • The combination of multiple perturbations is effective to improve ensemble performance at the nonlinear convective-scale. • Ensemble sizes larger than 50 members are required to capture extreme scenarios appropriately. [ABSTRACT FROM AUTHOR]