Abstract
The exponential dispersion model (EDM) has been demonstrated as an effective tool for quantifying rainfall dynamics across monthly time scales by simultaneously modelling discrete and continuous variables in a single probability density function. Recent applications of the EDM have included development and implementation of statistical software packages for automatically conditioning model parameters on historical time series data. Here, we advance the application of the EDM through an analysis of rainfall records in the North American Laurentian Great Lakes by implementing the EDM in a Bayesian Markov chain Monte Carlo (MCMC) framework which explicitly acknowledges historic rainfall variability and reflects that variability through uncertainty and correlation in model parameters and simulated rainfall metrics. We find, through a novel probabilistic assessment of skill, that the EDM reproduces the magnitude, variability, and occurrence of daily rainfall, but does not fully capture temporal autocorrelation on a daily time scale. These findings have significant implications for the extent to which the EDM can serve as a tool for supporting regional climate assessments, for downscaling regional climate scenarios into local‐scale rainfall time series simulations, and for assessing trends in the historical climate record. Copyright © 2012 Royal Meteorological Society