<p>Accurate subseasonal precipitation prediction is crucial for risk alleviation in agriculture, water resources, and disaster management within the densely-populated Asian summer monsoon region. In this study, we conduct a multimodel ensemble (MME) prediction of Asian summer monsoon precipitation (ASMP) by utilizing subseasonal reforecasts from six subseasonal-to-seasonal (S2S) models. A comprehensive evaluation within a seamless framework demonstrates that the MME outperforms any individual model in deterministic prediction across all lead times. A distinct regional heterogeneity becomes evident: the Maritime Continent (MC) displays the highest prediction skill, whereas the East Asia summer monsoon (EASM) presents the lowest. Probabilistic assessment suggests that the prediction skill is higher for strong anomalous precipitation events compared to near-normal events, primarily due to enhanced resolution rather than reliability. A generally positive correlation exists between deterministic and probabilistic skills, although its intensity varies regionally. The potential of the precipitation prediction is greater over the EASM than over the MC. Further analysis reveals that more accurate forecasting of lower- and upper-level zonal winds and sea surface temperature is pivotal for enhancing EASM precipitation prediction. Moreover, the MME effectively mitigates under-dispersion and improves reliability, approaching the ideal ensemble calibration. These findings underscore the significance of the MME approach and offer a scientific foundation for formulating customized forecasting strategies across different regions and event types.</p>