As one of the most significant circulation systems over the Northern Hemisphere in the cold season, the East Asian winter monsoon (EAWM) has been broadly investigated from the seasonal-mean perspective, while subseasonal variations in the EAWM still remain ambiguous. Based on Season-reliant Empirical Orthogonal Function (S-EOF) analysis, this study shows that the subseasonal strength reversal of the EAWM (SR-EAWM), featuring a weaker (or stronger) EAWM in early winter (December) and a stronger (or weaker) EAWM in late winter (January-February), is a distinct leading mode of the month-to-month variation of the EAWM. The weak-to-strong SR-EAWM is characterized by an anomalous low over Eurasia and a weakened East Asian major trough (EAT) in early winter, with an intensified Siberian High and a deepened EAT in late winter. The SR-EAWM is preceded by surface air temperature anomalies over Davis Strait (DST) and those over central-eastern North America (CENAT) in September-October. The DST mainly influences the SR-EAWM in early winter through a "sea ice bridge" of the November Baffin Bay sea ice concentration anomaly (BBSIC). The BBSIC could intensify the DST in December by altering surface heat flux, thus exciting a downstream atmospheric response and modulating the strength of the EAT in early winter. The CENAT affects the SR-EAWM in late winter by inducing an "ocean bridge" of the western North Atlantic sea surface temperature anomaly (WNASST). The WNASST can persist into late winter and then significantly affects the SR-EAWM by regulating Eurasian circulation anomalies and the downstream EAT. The bridge roles of the BBSIC and WNASST can be further verified by a linear baroclinic model. Finally, two physical-empirical models are established using the DST/BBSIC and the CENAT indices, respectively. Both exhibit promising prediction skills. The results highlight that the DST, BBSIC, and CENAT are crucial predictability sources for the SR-EAWM.

East Asian winter monsoon;subseasonal variation;surface air temperature;seasonal prediction