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The Base-Flow Indices (BFI), indicating surface and groundwater interaction, play a significant role in the hydrological cycle. The values vary widely across the globe, and are expected to change in the context of a changing climate. Predicting global “natural” BFI is challenging due to limited observations reflecting natural impacts. This study aims to predict annual BFI and their trends for a compiled dataset of annual streamflow and meteorological data covering the period 1982-2020 for more than 2250 small unregulated catchments worldwide. BFI were derived using three digital filtering methods, resulting in a trend of-0.0009±0.01 per decade over the last four decades. To predict annual BFI and their trends in ungauged catchments, a Random Forest Regression approach was employed, incorporating static attributes and meteorological time series data as model inputs. Five-fold cross-validation demonstrated the …