We investigate the sensitivity of precipitation characteristics (mean, extreme and diurnal cycle) to a set of uncertain parameters present in the Community Atmosphere Model (CAM5) that influence the qualitative and quantitative behavior of the cloud and aerosol processes in the model. We adopt both the Latin hypercube and quasi-Monte Carlo sampling approaches to effectively explore the high-dimensional parameter space and then conduct two large sets of simulations. One set consists of 1100 simulations (cloud ensemble) perturbing 22 parameters related to cloud physics and convection, and the other set consists of 256 simulations (aerosol ensemble) focusing on 16 parameters related to aerosols and cloud microphysics. A generalized linear model (GLM) is constructed to evaluate the response of precipitation to each parameter set. Results show that the global spatial pattern of precipitation variance among members is very similar between the two ensembles but the magnitude of variance is much larger in the cloud ensemble. The variability of extreme precipitation is much larger than that of the mean precipitation in response to parameter perturbations. The phase of precipitation diurnal cycle is insensitive to the two sets of parameters, even in the regions where the amplitude of precipitation diurnal cycle has large variation. Perturbations to aerosols have very minor impact on the diurnal cycle. For the 22 parameters perturbed in the cloud ensemble, the six having the greatest on the global mean precipitation are identified, three of which (related to the deep convection scheme) are the primary contributors to the total variance of the phase and amplitude of the precipitation diurnal cycle over land. Larger values of the three parameters make the rainfall peak earlier, implying that the CAM5 predicts too early deep convective precipitation peak which could potentially be corrected to some extent by lowering these parameters. The extreme precipitation characteristics are sensitive to a fewer number of parameters. The influence of individual parameters does not depend on the sampling approaches or concomitant parameters selected. Generally the GLM is able to explain more of the parametric sensitivity of global precipitation than local or regional features. The total explained variance for precipitation is primarily due to contributions from the individual parameters (75-90% in total). The total variance shows a significant seasonal variability in the mid-latitude continental regions.