Rivers are links connecting the biogeochemical processes among terrestrial, oceanic, and atmospheric carbon pools, and are important participants in the global water and carbon cycles. Riverine partial pressure of carbon dioxide (pCO2) is a key indicator reflecting the CO2 exchange process at the riverine water-air interface, which exhibits complex spatiotemporal variations due to the co-impacts of various natural and anthropogenic factors. However, the current understanding of the main controlling factors and their effects on riverine pCO2 is still very limited. In this study, the spatiotemporal distribution characteristics of riverine pCO2 were identified, and the relative contributions and controlling effects of the potential controlling factors were quantified and revealed using an interpretable machine learning method (boost regression tree (BRT) and accumulated local effects (ALE)), based on monthly datasets with high spatial resolution in the Han River Basin (HRB). Results indicated that multi-year average riverine pCO2 in the HRB showed an increasing trend from upstream to downstream, and was higher than the atmospheric average. The fluctuation type of multi-year monthly average riverine pCO2 in the HRB could be classified into three types based on the k-Shape clustering algorithm, with stationary (T1), unimodal (T2), and bimodal (T3) structures, respectively. The BRT model effectively simulated the multi-year average and multi-year monthly average values of riverine pCO2 in the HRB, showing high performances (r > 0.86, NSE > 0.75) and acceptable errors (MAE < 212.18 μatm, RMSE < 274.16 μatm) in replicate experiments. Multi-year average riverine pCO2 was mainly controlled by temperature factors, with a total relative contribution rate of about 66.1%. Relative contributions of controlling factors for multi-year monthly average riverine pCO2 varied greatly among each fluctuation type, while temperature factors still played a critical role (approximately 26.6% ~ 46.9%). Vegetation and water quantity factors had high contributions in type T2 and T3, respectively, while the importance of water quality factors was relatively limited (less than about 20.1%). The nonlinear and non-monotonic relationships between riverine pCO2 and its potential controlling factors were revealed based on ALE analysis results, and showed significant differences between multi-year average and multi-year monthly average scales, as well as between different fluctuation types. This study revealed the complex spatiotemporal variations of the main controlling factors and their effects on riverine pCO2 in the HRB, improving the understanding of riverine carbon cycle process.