Abstract. Changing climate leads to change of temporal dynamics of hydrological systems by affecting the catchment conditions. Considering climatic variations when calibrating a hydrological model can improve model performance, which allows parameter sets to vary according to sub-periods with different climate conditions. This study has explored climatic intra-annual variations by using two classification approaches to recognize the sub-periods with similar climatic patterns, Calendar-Based Grouping (CBG) method and Fuzzy C-Means (FCM) algorithm. The model performances of the sub-annual calibration schemes based on these two approaches are compared using the conceptual model IHACRES. The effect of time scales on sub-annual calibration schemes was also studied. Results indicate that the sub-annual calibration scheme based on CBG method performs better than that based on Rainfall-dominated FCM algorithm, since the CBG method has a better performance in recognizing temperature pattern, and the main source of catchment change is from the change of vegetation, which is mainly affected by temperature in the study site. The optimal time scale is dependent on the sub-annual calibration scheme, with bimonthly for CBG method and Temperature-dominated FCM algorithm and seasonal for Rainfall-dominated FCM algorithm. Overall, when using sub-annual calibration schemes, the selection of the partitioning method and time scale is very important to model performances