Correlations of the catch per unit effort (CPUE) (based on the catch data of skipjack tuna, caught using the purse seine technique in the west-central Pacific Ocean) with spatial-temporal factors (year, month, latitude, and longitude) and environmental factors (sea surface temperature, SST; sea surface height, SSH; oceanic nino index, ONI; and chlorophyll-a, Chl-a) were analyzed, and the relative importance of CPUE was estimated using two different types of models (Generalized additive model:GAM, and Boosted regression tree:BRT). The results showed that longitude is the most important factor in determining the importance of CPUE using GAM, accounting for more than 50% of the total CPUE, while latitude, year, and month had decreasing importance in the order mentioned. SSH is the most important environmental factor in GAM, and ONI, SST, and Chl-a are less important in determining the importance of CPUE. The result of BRT was similar to that of GAM; longitude is the most important spatial-temporal factor, accounting for 60% of the total importance of CPUE, while year, latitude, and month were of less importance, with their importance decreasing in the order mentioned. ONI is the most important environmental factor in BRT, followed by SSH, SST, and Chl-a, in that order. In conclusion, the two types of models can effectively reflect the influence of CPUE. ENSO induced oceanographic variation will change the abundance distribution of skipjack tuna; so, ONI should be included in fishery forecasting models to improve the accuracy of prediction in future.