Stochastic precipitation generators (SPGs) are a class of statistical models
which generate synthetic data that can simulate dry and wet rainfall stretches
for long durations. Generated precipitation time series data are used in
climate projections, impact assessment of extreme weather events, and water
resource and agricultural management. We construct an SPG for daily
precipitation data that is specified as a semi-continuous distribution at every
location, with a point mass at zero for no precipitation and a mixture of two
exponential distributions for positive precipitation. Our generators are
obtained as hidden Markov models (HMMs) where the underlying climate conditions
form the states. We fit a 3-state HMM to daily precipitation data for the
Chesapeake Bay watershed in the Eastern coast of the USA for the wet season
months of July to September from 2000--2019. Data is obtained from the
GPM-IMERG remote sensing dataset, and existing work on variational HMMs is
extended to incorporate semi-continuous emission distributions. In light of the
high spatial dimension of the data, a stochastic optimization implementation
allows for computational speedup. The most likely sequence of underlying states
is estimated using the Viterbi algorithm, and we are able to identify
differences in the weather regimes associated with the states of the proposed
model. Synthetic data generated from the HMM can reproduce monthly
precipitation statistics as well as spatial dependency present in the
historical GPM-IMERG data