Climate Dynamics

, Volume 38, Issue 7, pp 1375–1387

Statistical significance of trends in monthly heavy precipitation over the US


    • Computational Earth SciencesOak Ridge National Laboratory
  • Gerald R. North
    • Department of Atmospheric SciencesTexas A&M University
  • R. Saravanan
    • Department of Atmospheric SciencesTexas A&M University
  • Marc G. Genton
    • Department of StatisticsTexas A&M University

DOI: 10.1007/s00382-011-1091-4

Cite this article as:
Mahajan, S., North, G.R., Saravanan, R. et al. Clim Dyn (2012) 38: 1375. doi:10.1007/s00382-011-1091-4


Trends in monthly heavy precipitation, defined by a return period of one year, are assessed for statistical significance in observations and Global Climate Model (GCM) simulations over the contiguous United States using Monte Carlo non-parametric and parametric bootstrapping techniques. The results from the two Monte Carlo approaches are found to be similar to each other, and also to the traditional non-parametric Kendall’s τ test, implying the robustness of the approach. Two different observational data-sets are employed to test for trends in monthly heavy precipitation and are found to exhibit consistent results. Both data-sets demonstrate upward trends, one of which is found to be statistically significant at the 95% confidence level. Upward trends similar to observations are observed in some climate model simulations of the twentieth century, but their statistical significance is marginal. For projections of the twenty-first century, a statistically significant upwards trend is observed in most of the climate models analyzed. The change in the simulated precipitation variance appears to be more important in the twenty-first century projections than changes in the mean precipitation. Stochastic fluctuations of the climate-system are found to be dominate monthly heavy precipitation as some GCM simulations show a downwards trend even in the twenty-first century projections when the greenhouse gas forcings are strong.

Copyright information

© Springer-Verlag 2011