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[Display omitted] • Five discharge datasets have median correlation coefficients > 0.6 with the observations. • Overestimation of monthly mean flow is more common than underestimation. • GRFR showed the best estimate at around 40% of the locations. • None of the models provides good estimates for rivers and streams in arid regions. High-resolution global flow discharge datasets are critical to understand the terrestrial water cycle and river ecosystem services. Recently, a number of global hydrological models have been developed to capture global river flow dynamics at locally relevant scales. However, the relative accuracy of these simulated flow discharges is largely unknown. In this paper, using the largest observed streamflow datasets from > 26,000 gauging stations, we evaluated the performance of monthly and sub-monthly flow variation (1980–2014) generated from five major global hydrological models, Catchment-based Macro-scale Floodplain model (CaMa-Flood), Global Flood Awareness System (GloFAS-reanalysis), Global Reach-level Flood Reanalysis (GRFR), PCRaster GLOBal Water Balance model (PCR-GLOBWB), and Water Global Assessment and Prognosis (WaterGAP). Significant spatial heterogeneity in model performance was identified. We found that estimates by all five discharge datasets have a relatively high correlation with the observations (medians > 0.6). But none of them showed adequate correlations between simulated and observed flows for systems in Southwest or Central U.S.A., South Africa, or South Australia. Across all models, overestimation of monthly mean flow was more common than underestimation and high flows at a sub-monthly scale were more accurately characterized than low flows. Performance of all models was poor in Africa and Oceania. No single model performed universally best at all gauging stations, but GRFR showed the best estimate at around 40 % of the locations. None of the models provided satisfying estimates for rivers and streams in arid regions. We also found a significant negative effect of flow regulation on the predictive accuracy of all models. Our study highlights the importance of thorough evaluation of global discharge datasets before their local and regional application and provides guidance for future enhancement of global hydrological models. [ABSTRACT FROM AUTHOR]