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• Daily flow statistics (indicators) are key to understanding flow-ecology interaction. • This study examined whether monthly data can be used to describe daily flow regimes. • A suite of 173 flow indicators were converted to a monthly timestep where possible. • Around half of monthly indicators had strong correlations with their daily versions. • Using only monthly data, good predictions were achieved for 90% of daily indicators. Flow indicators based on daily flow data are commonly used to characterize the hydrologic regimes of rivers and to investigate links between flow and ecological condition. Discharge data measured at a daily timestep is considered essential for such investigations because there is an underlying premise that longer timesteps (e.g. monthly) are not suitable for capturing the variability in flow behaviour of most relevance to ecologically important processes. This paper investigates this assumption by examining the information content of daily and monthly streamflow data for a diversity of perennial, intermittent and ephemeral flow regimes across Australia, using a novel approach of comparing daily and monthly flow indicators rather than observed flow timeseries. Results show that many flow indicators derived using daily and monthly data are strongly correlated (Spearman ρ greater than 0.9). Partial least squares regression shows that the majority of daily flow indicators can be predicted using monthly flow indicators, and that as few as six monthly flow indicators can be used to estimate a wide range of daily flow characteristics, including those relating to flow magnitudes, and durations of high and low flow events. Daily indicators relating to the frequency and seasonal timing of high and low flows cannot be predicted as accurately (R2 between 0.4 and 0.9), suggesting that information on these facets of the flow regime is partially lost when aggregating to a monthly timestep. Results from this study point to the utility of monthly-based flow indicators in river ecology, opening new opportunities to overcome limitations in gauge network coverage and data resolution in many parts of the world. [ABSTRACT FROM AUTHOR]