The Amazon forest plays an important role in the tropical circulation and global water and biogeochemical cycles. However, the common Amazon precipitation biases in global climate models undermine our ability to understand future changes in the Amazon and their global consequences. The Community Earth System Model exhibits a persistent wet season dry bias over the southwestern Amazon. The bias is also characterized by inaccurate diurnal timing and intensity of rainfall events. Analysis of a diverse set of simulations suggests that the bias originates from the land model and/or deep convection parameterization. A known land model bias in the partitioning of surface energy fluxes is found not to contribute significantly to the rainfall bias. Key model processes are identified to be the low‐level divergent circulation controlling horizontal moisture flow and the sensitivity of the modeled deep convection to the lower tropospheric moisture. The lower tropospheric moisture divergence dictates the spatiotemporal distributions of convective precipitation through parameterized entrainment mixing. While the entrainment effect improves the rainfall diurnal cycle, it suppresses convective precipitation under low‐level moisture divergence. Persistent moisture divergence is found over the southwestern Amazon and limits parameterized convective precipitation, particularly in the late afternoon, although uncertainty exists in the low‐level flow in the simulation and reanalyses. One way to sustain rainfall in the presence of low‐level divergence is a convective trigger based on both the moisture and temperature changes in the free troposphere, which is shown to help in reducing the dry bias.