Global warming is a significant challenge of the 21st century, driving notable changes in weather patterns. On the other hand, the Interdecadal Pacific Oscillation (IPO) is a remarkable climatic mode of variability that impacts interdecadal climate patterns and the rate of global warming. This study introduces the extreme gradient boosting (XGBOOST) feature important metric, to disentangle and rank the fingerprints of global warming and IPO on the seasonal precipitation trends in Ohio, United States, a region characterized by variable weather. Using monthly precipitation data from 55 weather stations spanning 1960–2023, seasonal average trends for boreal winter, spring, summer, and autumn were analyzed using Theil-Sen’s Slope method, and statistical significance was tested at the 95% confidence level. Results indicate a significant increase in precipitation during winter (0.15 mm/decade) and summer (0.13 mm/decade), while no statistically significant changes were observed for spring and autumn. Correlation analysis revealed that 56.4% of the stations showed statistically significant positive correlations between global warming signals and increased winter precipitation. In comparison, 40% of the stations negatively correlated with the IPO during winter. Therefore, global warming and the negative IPO phase are associated with the observed increase in winter precipitation in most of the analyzed stations. In 60% of the stations, including stations impacted by the lake-effect snow, the XGBOOST model showed that the fingerprint of global warming ranked higher than the IPO. This indicates that global warming has a stronger association with the observed positive winter precipitation trend in most stations, and the IPO's net effect is limited to a smaller number of stations (i.e., 40%). These findings highlight that Ohio’s winters are becoming wetter with global warming remarkably contributing to it.