SignificanceIn nature, precipitation exhibits a significant variance with many extremes of precipitation, while climate models predict a smaller variance in precipitation with a bias toward light rain. Here, using a machine learning approach, we show that 1) we can implicitly learn relevant information about the role of the organization of clouds on precipitation, and 2) including this information can significantly improve precipitation prediction in climate models. Our implicitly learned organization metric explains precipitation stochasticity almost entirely and could replace a stochastic parameterization in climate models. Additionally, this metric exhibits a temporal correlation, caused by the persistence of cloud structure, and thus can be predicted using its own history.