  Modelling of precipitation, including extremes, is important for hydrological
and agricultural applications. Traditionally, because of large sample
properties for data over a large threshold value, generalised Pareto (GP)
distributions are often used for modelling extreme rainfall. It can be shown
that under certain conditions the generalised hyperbolic (GH) distributions can
approximate the power law decay of the GP distribution in the tails. Given
their flexible form, this raises the possibility that distributions from the GH
family serve as a model for the entire rainfall distribution thus avoiding the
need to select a threshold. In this paper, we use a flexible censored
hierarchical model that leverages the GH distribution to accommodate data
subject to heavy tails and an excessive number of zeros. The fitted model
allows estimation of probabilities and return periods of the rainfall extremes,
and it produces narrower credible intervals in the tails than the traditional
GP method. The model not only fits the tails of the rainfall distribution, but
fits the whole distribution very well. It also efficiently represents
short-term dependencies in the data so it is suitable for evaluating duration
over and below thresholds as well as duration of zero rainfall.
