Rainfall exhibits irregular variation over a wide range of space-time scales that defies a simple mathematical description. Probabilistic models provide a natural framework for expressing the unpredictable space-time variability of rain. However, constructing such a model that fits the statistical properties of precipitation data accurately over a relevant range of space-time averaging scales and a variety of climatological conditions has proved to be a great challenge. In this talk I’ll give a synopsis of the efforts undertaken here in the Laboratory over the past two decades to construct simple statistical models that try to capture various aspects of the irregular space-time variation of rain in terms of a random process. These include: (i) a spectral model of the dynamical evolution of the rain field in terms of a so-called anomalous or fractional diffusion process that is designed to describe the covariance statistics of rain at various space-time averaging scales, and (ii) a model of the full probability distribution of rain rate including intermittence (i.e. the zeroes of rain) in terms of a new class of distribution functions that naturally accounts for the multifractal nature of rain. I’ll also discuss how these models can be practically useful in obtaining error estimates for various rainfall inter-comparison scenarios involving data from satellite and ground measurements and climate models. It is expected that these modeling efforts, especially the spectral model, will play an important role in the radar-gauge inter-comparison at the GPM ground validation site that is currently being set up in Wallops Island, Virginia.