Objective performance metrics that measure precipitation variability across timescales from sub-daily to interannual are presented and applied to Historical simulations of Coupled Model Intercomparison Project phase 5 (CMIP5) and CMIP6 models. Three satellite-based precipitation estimates (IMERG, TRMM, and CMORPH) are used as reference data. We apply two independent methods to estimate temporal variability of precipitation and compare the consistency in their results. The first method is derived from power spectra analysis of 3-hourly precipitation, measuring forced variability by solar insolation (diurnal and annual cycles) and internal variability at different timescales (sub-daily, synoptic, sub-seasonal, seasonal, and interannual). The second method is based on time-averaging and facilitates estimating the seasonality of sub-daily variability. Supporting the robustness of our metric, we find a near equivalence between the results obtained from the two methods when examining simulated-to-observed ratios over large domains (global, tropics, extra-tropics, land or ocean). Additionally, we demonstrate that our model evaluation is not very sensitive to the discrepancies between observations. Our results reveal that CMIP 5 and 6 models in general overestimate the forced variability while they underestimate the internal variability, especially in the tropical ocean and higher frequency variability. The underestimation of Sub-daily variability is consistent across different seasons. The internal variability is overall improved in CMIP6, but remains underestimated, and there is little evidence of improvement in forced variability. Increased horizontal resolution results in some improvement of internal variability at sub-daily and synoptic time scales, but not at longer timescales.