Abstract High Mountain Asia (HMA), spanning from the Hindu Kush to the Tibetan Plateau, encompasses tropical and subtropical regions highly susceptible to extreme precipitation events and associated hazards. El Niño–Southern Oscillation (ENSO) is one of the dominant external climate modes that influence subseasonal to seasonal precipitation over HMA through various dynamical pathways. We hypothesize three possible ENSO-driven teleconnection pathways impacting HMA precipitation and test their causality using a data-driven causal discovery method, PCMCI+ , an improved version of the Peter and Clark Momentary Conditional Independence algorithm. The three physically reasoned ENSO- driven teleconnection pathways are (1) extratropical Rossby wave response (EWP), (2) tropical moisture transport from the Indian ocean (TMP) and (3) the subtropical westerly jet modulation (SJP) towards HMA. Contrary to most prior studies that rely on simple correlation analysis to establish ENSO-HMA precipitation relationships, PCMCI + offers a rigorous causal discovery method for high dimensional interdependent time series, based on graphical causal models for establishing causal links and estimating their strength. Our analysis shows that HMA November precipitation is modulated by an ENSO extratropical Rossby wave teleconnection and by tropical moisture transport (EWP and TMP), while March precipitation is influenced through the subtropical jet (SJP). Moreover, by quantifying the causal effect of ENSO with robust causal network guided regression, we establish how a change on ENSO would propagate to variations in HMA precipitation. These findings offer critical insights for improving winter precipitation forecasts over HMA, diagnosing physics-based models, and examining future changes under internal and forced climate variability.
Borah et al. (2026) studied this question.