Detection of non-stationarity in precipitation extremes using a max-stable process model

Non-stationarity in extreme precipitation at sub-daily and daily timescales is assessed using a spatial extreme value model based on max-stable process theory. This approach, which was developed to simulate spatial fields comprising observations from multiple point locations, significantly increases the precision of a statistical inference compared to standard univariate methods. Applying the technique to a field of annual maxima derived from 30 sub-daily gauges in east Australia from 1965 to 2005, we find a statistically significant increase of 18% for 6-min rainfall over this period, with smaller increases for longer duration events. We also find an increase of 5.6% and 22.5% per degree of Australian land surface temperature and global sea surface temperature at 6-min durations, respectively, again with smaller scaling relationships for longer durations. In contrast, limited change could be observed in daily rainfall at most locations, with the exception of a statistically significant decline of 7.4% per degree land surface temperature in southwest Western Australia. These results suggest both the importance of better understanding changes to precipitation at the sub-daily timescale, as well as the need to more precisely simulate temporal variability by accounting for the spatial nature of precipitation in the statistical model. (C) 2011 Elsevier B.V. All rights reserved.