The calibration of global hydrological models has been attempted for over two decades, but an effective and generic calibration method has not been proposed. In this study, we investigated the application of Approximate Bayesian Computation (ABC) to calibrate the H08 global hydrological model by running global simulations with 5000 randomly generated sets of four sensitive parameters. This yielded satisfactory results for 777 gauged watersheds, indicating that ABC can be used to optimize H08 parameters to calibrate individual watersheds. We tested the identifiability of the parameters to yield satisfactory representations of hydrological functions based on Köppen’s climate classification (“climate-based” calibrations hereafter) We aggregated 5000 simulation results per catchment based on the 11 Köppen climate classes, then selected the parameters that exceeded the Nash–Sutcliffe efficiency (NSE) scores predefined by the acceptance ratio for each climate class. Our results indicate that the number of stations showing satisfactory (NSE > 0.0) and good (NSE>0.5) performances were 480 and 234 (61.7% and 30.1% of total stations, respectively), demonstrating the effectiveness of climate-based calibration. We also showed that the climate-based parameters outperformed the default and global parameters in terms of representativeness (global-scale differences of hydrological properties among climate classes) and robustness (consistency in yielding satisfactory results for watersheds in the same climate class). The identified parameters for 11 Köppen climate classes showed consistency with the physical interpretation of soil formation and efficiencies in vapor transfer with a wide variety of vegetation types, corroborating the strong influence of climate on hydrological properties.