Sources of Potential Predictability for July–August 2025 Extreme Precipitation Anomalies Over Northern China: Roles of the Western Pacific and North Atlantic–Barents–Kara Sea Sectors

Abstract Accurately predicting persistent extreme precipitation remains a major challenge for subseasonal‐to‐seasonal (S2S) prediction models. This study identifies two distinct sources of predictability for the July and August 2025 extreme precipitation anomalies over northern China (NC) and reveals how these remote signals improved the prediction. By separating S2S models into high‐skill and low‐skill groups, we show that the July predictability was mainly linked to tropical forcing over the western Pacific (WP), whereas the August predictability was primarily associated with midlatitude wave‐train activity from the North Atlantic (NA) and Barents–Kara Seas (BKS). In July, high‐skill models better captured typhoon‐induced convection over the subtropical WP and the associated northward‐propagating Pacific–Japan wave train. This circulation response favored westward moisture transport from the warm WP along the edge of the northward‐extended western Pacific subtropical high, thereby improving the prediction of NC precipitation. In August, high‐skill models more realistically represented eastward‐propagating midlatitude wave trains from the NA and BKS. These wave trains enhanced energy conversion over NC and contributed to a more accurate prediction of the August extreme precipitation anomaly. Although the S2S models showed clear indications of predictability, most models underestimated the precipitation intensity, highlighting persistent model deficiencies in representing the amplitude of extreme rainfall. Nudging experiments further quantified the contribution of these remote predictable signals. Correcting WP circulation and convection‐related anomalies explained approximately 78% of the July intensity improvement, while correcting NA and BKS wave‐train anomalies explained approximately 46% and 66% of the August improvement, respectively.