A novel method is presented for model uncertainty estimation using machine learning techniques and its application in rainfall runoff modeling. In this method, first, the probability distribution of the model error is estimated separately for different hydrological situations and second, the parameters characterizing this distribution are aggregated and used as output target values for building the training sets for the machine learning model. This latter model, being trained, encapsulates the information about the model error localized for different hydrological conditions in the past and is used to estimate the probability distribution of the model error for the new hydrological model runs. The M5 model tree is used as a machine learning model. The method is tested to estimate uncertainty of a conceptual rainfall runoff model of the Bagmati catchment in Nepal. In this paper the method is extended further to enable it to predict an approximation of the whole error distribution, and also the new results of comparing this method to other uncertainty estimation approaches are reported. It can be concluded that the method generates consistent, interpretable and improved model uncertainty estimates.