Low visibility (VIS) involves haze, fog, rain, snow, or their combination, while VIS prediction commonly treats aerosol and hydrometeor extinction separately. Observations and the coupled chemistry–weather model are used to quantify their contributions to low VIS in four subregions over eastern China in November 2024. Haze, fog, rain, and snow can all cause low VIS (≤10 km). For 3 < VIS ≤ 10 km and 1 < VIS ≤ 3 km, haze is the most frequent weather record in Harbin and Beijing, whereas fog prevails in Shanghai and Wuhan; for VIS ≤ 1 km, fog dominates in four cities. For 3 < VIS ≤ 10 km, aerosols contribute 87.1–97.3% of the model-estimated total extinction and hydrometeors 2.7–12.8%; for 1 < VIS ≤ 3 km, aerosols contribute 33.9–68.1% and hydrometeors 31.9–66.1%; for VIS ≤ 1 km, hydrometeors contribute 71.6–87.4%, mainly from fog, and fog and snow dominate Northeast China, while aerosols contribute 12.3–28.4%. Jointly considering aerosol and hydrometeor extinction generally produces the lowest root-mean-square error across all VIS grades and the highest threat scores for all VIS levels, especially for VIS ≤ 3 km. These results demonstrate that including both aerosol and hydrometeor extinction is essential for reliable low-VIS prediction.

Low visibility event, as a disastrous weather, has great impacts on traffic and transportation, aircraft, and people’s daily life, etc. Timely and accurate forecasts of low visibility events are urgently needed and meaningful. The reasonable algorithm of atmospheric extinction in atmospheric chemistry models is the basis for quantitatively predicting low visibility. The revised IMPROVE algorithm (RIMP) of atmospheric extinction is incorporated into the chemistry-weather interacted model GRAPES_Meso5.1/CUACE CW V1 to improve the prediction of low visibility events (LVEs) in the urban agglomerations in eastern China, which is compared with the original IMPROVE algorithm (OIMP) used in this model. The study results show that the RIMP effectively reduces the overestimation of low visibility prediction by OIMP in general, leading to a decrease of root-mean-square errors (RMSEs) and an increase of Threat Score (TS) of visibility less than 3 km, 5 km, and 10 km overall both at regional and city scales in varying degrees due to its more detailed processing of aerosols’ size, optical feature and hygroscopic growth; The improvements of visibility prediction of LVEs by RIMP depends on the combined contribution of high relative humidity (RH) and PM2.5 instead of single high RH or PM2.5. The relative contributions of RH and PM2.5 concentration on different levels of low visibility are different in Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions due to their different RH and PM2.5, which leads to the different improvement of RIMP in the two regions. The larger improvements by RIMP occur for visibility less than 5 km in BTH, while in YRD, the larger improvements by RIMP occur for visibility less than 10 km and greater than 5 km. Moreover, the improvements by RIMP were more evident with higher RH conditions in both regions. The uncertainty created by the extinction algorithm is one important factor of the multiple factors affecting LVEs prediction; accurate modeling of high RH near saturation is also very important for LVEs prediction.