Despite the Variable Infiltration Capacity (VIC) model being used for decades in the hydrology community, there are still model parameters whose sensitivities remain unknown. Additionally, understanding the factors that control spatial variations in parameter sensitivities is crucial given the increasing interest in obtaining spatially coherent parameter fields over large domains. In this study, we investigate the sensitivities of 43 soil, vegetation and snow parameters in the VIC model for 101 catchments spanning the diverse hydroclimates of continental Chile. We implement a hybrid local-global sensitivity analysis approach, using eight model evaluation metrics to quantify sensitivities, with four of them formulated from runoff time series, two characterizing snow processes, and the remaining two based on evaporation processes. Our results confirm an overparameterization for the processes analyzed here, with only 12 (i.e., 28 %) parameters found to be sensitive, distributed among soil (7), vegetation (2) and snow (3) model components. Correlation analyses show that climate variables - in particular, mean annual precipitation and the aridity index - are the main controls on parameter sensitivities. Additionally, our results highlight the influence of the leaf area index on simulated hydrologic processes - regardless of the dominant climate types - and the relevance of hard-coded snow parameters. Based on correlation results and the interpretation of spatial sensitivity patterns, we provide guidance on the most relevant parameters for model calibration according to the target processes and the prevailing climate type. Overall, the results presented here contribute to an improved understanding of model behavior across watersheds with diverse physical characteristics that encompass a wide hydroclimatic gradient from hyperarid to humid systems.