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Regional sensitivity analysis, RSA, has been widely applied in assessing the parametric sensitivity of environmental and hydrological models, in part because of its inherent simplicity. In that spirit, this paper reports an example of an augmented approach to improve its utility in ranking parameter importance, beyond reliance solely on the univariate marginal distributions, to include parametric interactions. Both a deterministic and a stochastic model of the transmission of dengue, an important mosquito‐borne disease, were used to explore the effect of interactions to parameter importance ranking using random forests, a commonly used method based on decision trees. The importance ranking based on random forests was generally consistent with the ranking computed from earlier methods that only examined marginal distributions, but with increased importance shown by several interacting parameters. In addition …