Dynamic internal variability dominates uncertainty in modeling future extreme precipitation

Extreme precipitation (EP) is a major climate risk, yet its projections remain uncertain due to the combined influence of thermodynamic (TH) and dynamic (DY) processes. Using multi-model simulations under three emission scenarios, we separate TH and DY contributions to the annual maximum 1-day precipitation (Rx1Day) and quantify their uncertainties. TH consistently intensifies extremes with warming, while DY strongly modulates their magnitude and direction. DY processes dominate Rx1Day uncertainty, with internal variability within DY emerging as the leading contributor. Signal-to-noise ratio (SNR) analysis shows that the forced signal emerges more clearly for TH than DY, where chaotic variability fundamentally limits predictability. The strongest intensification occurs in equatorial regions, raising equity concerns for vulnerable populations. These results demonstrate that DY internal variability is the primary driver of EP uncertainty, highlighting limits to long-term predictability and the importance of properly representing natural dynamical fluctuations in future projections.