Reply on RC2

Abstract

For climate change impact assessment, many applications require very high-resolution data of precipitation consistent both in space and time for current as well as future climate. In this regard, stochastic weather generators are designed as a statisti- cal downscaling tool that can provide such data. Here, we adopt the framework of a precipitation generator of Kleiber et al. (2012), which is based on latent and transformed Gaussian processes, and propose an extension to that for a mountainous region with complex topography. The model is used to generate two-dimensional fields of precipitation with 1 km spatial and daily temporal resolution in a small region with highly complex terrain in the Austrian Alps. This study aims at evaluating the model for its ability to simulate realistic precipitation fields over the region using historical observations from a network of 29 meteorological stations as an input, discusses its added value over the original set-up and its limitations. Results show that the model generates realistic fields of precipitation with good spatial and temporal variability. The model is able to generate some of the difficult areal statistics useful for impact assessment such as areal dry and wet spells of different lengths and areal monthly mean of precipitation with great accuracy. The model also captures the inter-seasonal and intra-seasonal variability very well while the inter-annual variability is well captured in summer but largely underestimated in autumn and winter. The proposed model adds substantial value over the original modeling framework, specifically for the precipitation amount. The model is not able to reproduce realistic spatio-temporal characteristics of precipitation in autumn. We conclude that with further development, the model is a promising tool for downscaling precipitation in complex terrain for a wide range of applications in impact assessment studies.