The paper mainly has two contributions. First, a post-processor framework is proposed to improve hydrologic model accuracy. The key is to characterize possible connections between model inputs and errors. Based on results, it is also possible to replace the time-consuming model calibration step using our post-processor framework. Second, a window selection method is proposed to handle nonstationary data. A window size is chosen containing stable data using a measure named DS proposed by us.