How well do general circulation models represent low-frequency rainfall variability?

General circulation models (GCMs) provide reliable simulations of global‐ and continental‐scale atmospheric variables, yet have limited skill in simulating variables important for water resource management at regional to catchment scales. GCM simulations suffer from a range of uncertainties leading to transient (changing over time) and systemic (consistent over time) biases in the output when compared to observed records. An important GCM bias in managing water resources infrastructure is the underrepresentation of low‐frequency variability a characteristic central to the simulation of floods and droughts. This study presents a performance metric, the aggregated persistence score (APS), which is used to assess the reliability of GCMs in simulating low‐frequency rainfall variability. The APS identifies regions where GCMs poorly represent the amount of variability seen in the observed precipitation. This study calculated the APS at monthly aggregations for GCM precipitation as well as GCM precipitation that was bias‐corrected to better represent low‐frequency variability. It was found that there were (1) large spatial variations in the skill of GCMs to capture observed rainfall persistence, (2) widespread undersimulation of rainfall persistence characteristics in GCMs, and (3) substantial improvement in rainfall persistence after applying bias correction. Key Points Large spatial variations in the skill of GCMs to capture observed persistence Widespread under‐simulation of rainfall persistence characteristics in GCMs Substantial improvement in persistence after applying nested bias correction