Seasonal streamflow forecasts for snowmelt-dominated catchments often rely on snowpack data, which are not always available and are prone to errors. Our study evaluates near-real-time global snow estimates and climate oscillation indices for predictions in the data-scarce mountains of central Asia. We show that climate indices can improve prediction accuracy at longer lead times, help offset snow data uncertainty, and enhance predictions where streamflow depends on in-season climate variability.