Changepoint detection in daily precipitation data


  • This article is published in Environmetrics as a special issue on Advances in Statistical Methods for Climate Analysis, edited by Peter Guttorp, University of Washington, Norwegian Computing Center, Stephan R. Sain, National Center for Atmospheric Research, Christopher K. Wikle, University of Missouri.

Robert Lund, Department of Mathematical Sciences, Clemson University, Clemson, SC 29634-0975, U.S.A. E-mail:


This paper introduces a method to identify an undocumented changepoint time in a daily precipitation series. A two-state Markov chain is used to induce dependence in the precipitation amounts; our dynamics allow for seasonality in the daily observations, a structure inherent to many nonequatorial region series. No current precipitation changepoint techniques exist that consider day-to-day dependencies, the zero support set aspect (the fact that most measurements are zero), and the periodic dynamics of the problem. The test statistic is constructed by applying cumulative sum methods to a strategically devised set of one-step-ahead prediction residuals. The methods are robust to distributional assumptions, requiring only seasonal mean and transition probability estimators. Simulations are presented that demonstrate the efficacy of the methods; application to two daily precipitation series is made. Copyright © 2012 John Wiley & Sons, Ltd.