To improve the spatial and temporal resolution of fishery forecast models and the resource utilization and economic benefits of Pacific saury (Cololabis saira), a generalized additive model (GAM) was used to fit the suitability index between the catch per unit effort (CPUE) and marine environmental variables, based on Chinese saury fishery and environmental data from the high seas of the northwest Pacific Ocean during July and November from 2013 to 2016. Weighted analysis was also conducted using boosted regression tree models to develop monthly habitat suitability index (HSI) models. The results indicated that the GAM can be reliably used to fit relationships between the suitability index and environmental variables and can obtain optimal environmental variable values. Weighted analysis showed that the three important environmental variables affecting CPUE were sea surface temperature gradient, sea surface temperature, and mixed layer depth. The weight of the sea surface temperature gradient was the highest during September to November (autumn). The overall accuracy of the HSI model test and evaluation stages were 82.0% and 73.2% respectively, reaching 87.7% and 77.9% in autumn, respectively. Furthermore, forecast accuracy was 89.4% in October during the main fishing season. The high-HSI areas were consistent with the fishing grounds of Pacific saury. Thus, the results show that the HSI model is suitable for forecasting the saury fishery and has a significant advantage in daily forecasting.