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To better understand the long-term changes of the East Asian summer monsoon precipitation (Pjja), quantitative reconstructions and model simulations are needed. Here, we develop continental-scale pollen-based transfer functions for Pjja with weighted averaging–partial least squares (WA-PLS) regression and a Bayesian multinomial regression method. We apply these transfer functions to a set of fossil pollen data from monsoonal China for quantitatively reconstructing the Pjja changes over the last 9500 years. We compare the reconstructions with Pjja simulations from a coupled atmosphere–ocean–sea ice general circulation model (the Kiel Climate Model, KCM). The results of cross-validation tests for the transfer functions show that both the WA-PLS model (r2 = 0.83, root mean square error of prediction (RMSEP) = 112.11 mm) and the Bayesian model (r2 = 0.86, RMSEP = 107.67 mm) exhibit good predictive performance. We stack all Pjja reconstructions from northern China to a summary curve. The stacked record reveals that Pjja increased since 9500 cal. yr BP, attained its highest level during the Holocene summer monsoon maximum (HSMM) at ~7000–4000 cal. yr BP and declined to present. The KCM output and the reconstructions differ in the early-Holocene (~9500–7000 cal. yr BP) where the model suggests higher Pjja than the reconstructions. Moreover, during the HSMM, the amplitude of the Pjja changes (~20–60 mm above present) in simulations is lower than the reconstructed changes (~70–110 mm above present). The rising (declining) Pjja patterns in reconstructions before (after) the HSMM are more pronounced and fluctuating than in simulations. Other palaeohydrological data such as lake-level reconstructions indicate substantial monsoon precipitation changes throughout the Holocene. Our results therefore show that the KCM underestimates the overall amplitude of the Holocene monsoon precipitation changes. [ABSTRACT FROM AUTHOR]