This study investigates the benefits of spatio-temporal analysis frequency separation (STAFS) strategies prior to bootstrapping periodically correlated time series. Analysis of time series data that contains periodically correlated (PC) principal components, such as seasonal, daily, or other cyclostationary processes, benefits from separating these components from other interfering frequencies or PC components. Bootstrapping allows estimation of a statistic's sampling distribution using random sampling with replacement, while block bootstrapping is a model-free resampling strategy for time series data and extensions of the block bootstrap help preserve the correlation structures of PC processes. Frequency separated periodic block bootstrapping (FSPBB) is introduced to separate different periodic components by frequency and bootstrap the PC components to improve estimation of the periodic characteristics of those component processes as well as the full time series. Data simulation studies are used to compare block bootstrapping results of periodically correlated time series with and without prior frequency separation.