Terrestrial
export of nitrogen is a critical Earth system process,
but its global dynamics remain difficult to predict at a high spatiotemporal
resolution. Here, we use deep learning (DL) to model daily riverine
nitrogen export in response to hydrometeorological and anthropogenic
drivers. Long short-term memory (LSTM) models for the daily concentration
and flux of dissolved inorganic nitrogen (DIN) were built in a coastal
watershed in southeastern China with a typical subtropical monsoon
climate. The DL models exhibited excellent accuracy for both DIN concentration
and flux, with Nash-Sutcliffe efficiency coefficients (NSEs) up to
0.67 and 0.92, respectively, a performance unlikely to be achieved
by generic process-based models with comparable data quality. The
flux model ensemble, without retraining, performed well (mean NSE
= 0.32–0.84) in seven distinct watersheds in Asia, Europe,
and North America, and retraining with multi-watershed data further
improved the lowest NSE from 0.32 to 0.68. DL interpretation confirmed
that interbasin consistency of riverine nitrogen export exists across
different continents, which stems from the similarities in rainfall–runoff
relationships. The multi-watershed flux model projects 0.60–12.4%
increases in the nitrogen export to oceans from the studied watersheds
under a 20% increase in fertilizer consumption, which rises to 6.7–20.1%
with a 10% increase in runoff, indicating the synergistic effect of
human activities and climate change. The DL-based method represents
a successful case of explainable artificial intelligence in environmental
science, providing a potential shortcut to a consistent understanding
of the global daily-resolution dynamics of riverine nitrogen export
under the currently limited data conditions.