Attribution of the 2025 Persistent Summer Rainfall over Northern China: Increasing Likelihood under Anthropogenic Forcing

An unusually persistent rainfall event was observed over northern China during July–August 2025. It was the most extreme event in the past 50 years, characterized by exceptional rainfall frequency and duration. Understanding of the occurrence, causes, and changing risks of such long-lasting events remains limited. In this study, using observations, reanalysis data, and climate simulations from the CMIP6 detection and attribution experiments, we characterize this event, examine the associated large-scale circulation background, and analyze the contribution of human influence. The rainfall anomalies were concentrated over Nei Mongol Zizhiqu (i.e., Inner Mongolia), Shanxi Province, and the Jing–Jin–Ji region, with the maximum centered in northwestern Shanxi, where precipitation was nearly twice the climatological mean. Both grid-based and regional extreme precipitation indices show that the 2025 event was distinguished primarily by exceptionally high rainfall frequency and long duration. It was primarily associated with an anomalous northwestward extension of the western Pacific subtropical high (WPSH), under which moisture transport from the adjacent eastern seas was enhanced. Event-based attribution indicates that anthropogenic forcing may have increased the likelihood of the 2025-like persistent summer rainfall over northern China, while a substantially stronger increase is projected under future warming. In particular, human influence enhances the risk of not only large precipitation anomalies but also unusually persistent regional rainfall. Under SSP2-4.5, the future likelihood is further amplified relative to the present-day climate by about 2.7 times for precipitation anomaly and 1.75 times for regional rainfall persistence. These findings suggest that the 2025-like persistent summer rainfall event over northern China will be more likely happening in a warming climate, underscoring the need for improved prediction and strengthened adaptation strategies.