Analysing Trends in Precipitation Extremes for a Network of Stations

Temporal trends in meteorological extremes are often examined by first reducing daily data to annual index values, such as 95th or 99th percentiles. Here, we report how this idea can be elaborated to provide an efficient test for trends at a network of stations. The initial step is to make separate estimates of tail probabilities of precipitation amounts for each combination of station and year by fitting a generalized Pareto distribution (GPD) to data above a user-defined threshold. The resulting time series of annual percentile estimates are subsequently fed into a multivariate Mann-Kendall (MK) test for monotonic trends. We performed extensive simulations using artificially generated precipitation data and noted that the power of tests for temporal trends was substantially enhanced when ordinary percentiles were substituted for GPD percentiles. Furthermore, we found that the trend detection was robust to misspecification of the extreme value distribution. An advantage of the MK test is that it can accommodate nonlinear trends, and it can also take into account the dependencies between stations in a network. To illustrate our approach, we used long time series of precipitation data from a network of stations in the Netherlands. KEY WORDS climate extremes; precipitation; temporal trend; generalized Pareto distribution; climate indices; global warming 1