This article is a great resource for understanding the purpose and limitations of bias correction. Bias is identified as the time independent component of the error (error is the difference between the simulated and observed climate). The authors highlight that bias-correction can not correct for incorrect representations of dynamical and/or physical processes. There are currently many bias-correction methods: quantile mapping, histogram equalization, and rank matching. These methods do not take into account oscillations on different time scales. This study proposes a modification of the existing methodology to perform bias-correction on different time scales. The cascade bias-correction method breaks down original processes into different time scales to avoid mixing. This breakdown is somewhat arbitrary, but monthly time scales are used in this study. A three-tier cascade is also investigated to allow for statistics on hourly, daily, and monthly time scales. The study found that the cascade bias-correction improves the bias-correction. This resource is useful for describing the necessary assumptions and limitations of bias-correction procedures for any modeling application. It also is useful for understanding the uncertainties associated with climate model output that has been bias-corrected.