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Nearest-neighbour resampling was used to generate multi-site sequences of daily precipitation and temperature in the Rhine basin. The simulation is conditional on the values of 3 continuous indices of the atmospheric circulation. An advantage of nearest-neighbour resampling is that the spatial correlations of the daily precipitation and temperature data are automatically preserved in the simulated data. Comparison of different resampling models showed that the simulation of the precipitation and temperature for a new day should not only be conditioned on the circulation characteristics of that day but also on the simulated precipitation and temperature for the previous day, in order to achieve the appropriate level of persistence and variability in the generated data. With a hydrological application in mind, 980 yr multi-site simulations of daily precipitation and temperature were performed conditional on a simulated time series of circulation indices that was obtained with a second resampling model. The distribution of the extreme 10 d area-average precipitation amounts in these long-duration simulations was compared with the distribution of the historical 10 d area averages. Again, the models in which the precipitation and temperature of the previously simulated day were taken into account performed best, but even these models somewhat underestimate the quantiles of the distribution of the 10 d area-average precipitation. The long-duration simulations demonstrate that nearest-neighbour resampling is capable of producing much larger 10 d areaaverage precipitation amounts than the historical maximum.

Multi-site simulation of daily precipitation and temperature conditional on the atmospheric circulation