This paper studies how to improve the accuracy of hydrologic models using machine learning models as post-processors and presents possibilities to reduce the workload to create an accurate hydrologic model by removing the calibration step. It is often challenging to develop an accurate hydrologic model, due to the time-consuming model calibration procedure and the non-stationarity of hydrologic data. Our findings show that the errors of hydrologic models are correlated with model inputs. Thus motivated, we propose a modeling error learning based post-processor framework by leveraging this correlation to improve the accuracy of a hydrologic model. The key idea is to predict the differences (errors) between the observed values and the hydrologic model predictions by using machine learning techniques. To tackle the non-stationarity issue of hydrologic data, a moving window based machine learning approach is proposed to enhance the machine learning error predictions by identifying the local stationarity of the data using a stationarity measure developed based on Hilbert-Huang transform. Two hydrologic models, the Precipitation-Runoff Modeling System (PRMS) and the Hydrologic Modeling System (HEC-HMS), are used to evaluate the proposed framework. Two case studies are provided to exhibit the improved performance over the original model using multiple statistical metrics.