Implementation and validation of a Wilks-type multi-site daily precipitation generator over a typical Alpine river catchment

Many climate impact assessments require high-resolution precipitation time series that have a spatiotemporal correlation structure consistent with observations, for simulating either current or future climate conditions. In this respect, weather generators (WGs) designed and calibrated for multiple sites are an appealing statistical downscaling technique to stochastically simulate multiple realisations of possible future time series consistent with the local precipitation characteristics and their expected future changes. In this study, we present the implementation and validation of a multi-site daily precipitation generator re-built after the methodology described in Wilks (1998). The generator consists of several Richardson-type WGs run with spatially correlated random number streams. This study aims at investigating the capabilities, the added value and the limitations of the precipitation generator for a typical Alpine river catchment in the Swiss Alpine region under current climate. The calibrated multi-site WG is skilful at individual sites in representing the annual cycle of the precipitation statistics, such as mean wet day frequency and intensity as well as monthly precipitation sums. It reproduces realistically the multi-day statistics such as the frequencies of dry and wet spell lengths and precipitation sums over consecutive wet days. Substantial added value is demonstrated in simulating daily areal precipitation sums in comparison to multiple WGs that lack the spatial dependency in the stochastic process. Limitations are seen in reproducing daily and multi-day extreme precipitation sums, observed variability from year to year and in reproducing long dry spell lengths. Given the performance of the presented generator, we conclude that it is a useful tool to generate precipitation series consistent with the mean climatic aspects and likely helpful to be used as a downscaling technique for climate change scenarios.