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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 <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 PM 2.5 instead of single high RH or PM 2.5. The relative contributions of RH and PM 2.5 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 PM 2.5 , which leads to the different improvement of RIMP in the two regions. The larger improvements by RIMP occur for visibility <5 km in BTH, while in YRD, the larger improvements by RIMP occur for visibility <10 km and >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. • The revised IMPROVE (RIMP) algorithm is applicated to the atmospheric chemistry model to improve low visibility prediction. • RIMP generally improves low visibility prediction than the original IMPROVE algorithm (OIMP). • The improvements in low visibility prediction by RIMP vary depending on different aerosol and humidity conditions. [ABSTRACT FROM AUTHOR]