One way to reduce predictive uncertainty due to the model structure is to incorporate information from several different models at once. Model aggregation can lead to a better prediction than a single model structure, as each model provides new information about the processes that are occurring. This paper presents an alternative by further developing an idea introduced by Marshall et al. (2006). A modeling framework that combines a number of individual model structures is presented. The approach, known as the hierarchical mixtures of experts (HME) framework, allows for a more sophisticated method of model aggregation by allowing individual models to be selected based on the preceding catchment conditions and also gives greater flexibility to the specification of the model errors. The modeling framework was previously shown to have potential as a modeling tool for assessing model components and identifying structural deficiencies in existing representations. This paper presents a basis for using the HME rainfall-runoff modeling framework for prediction or simulation. We illustrate the rationale behind the proposed framework using daily rainfall, evapotranspiration, and flow data from two small sized catchments (<150 km2) in the state of New South Wales, Australia. Two different forms of a conceptual rainfall-runoff model are used to represent the multiple component models of the framework. We investigate the usefulness of different catchment predictors to weight the individual models by assessing the resulting performance in a predictive sense. The usefulness of alternative models for the description of the model errors is shown. The study shows that given careful comparison of the possible mechanisms related to a switch in the catchment “state,” the proposed approach can be a useful predictive tool, giving an aggregated model simulation that is better than any individual model.