A multidimensional stochastic model is developed for the space‐time distribution of daily precipitation. The rainfall is linked to the atmospheric circulation patterns using conditional distributions and conditional spatial covariance functions. The model is a transformed conditional multivariate autoregressive AR(I) model, with parameters depending on the atmospheric circulation pattern. The model reproduces both the local rainfall occurrence probabilities and the distribution of the rainfall amounts at given locations, and the spatial dependence described with the help of cross‐covariances of the transformed series. Parameter estimation methods based on the moments of the observed data are developed. A simulation procedure for the model is also presented. Its link to atmospheric circulation patterns makes it suitable for local precipitation simulation under stationary and nonstationary arrivals of atmospheric circulation patterns such as climate change. The model is applied using the classification scheme of the German Weather Service which is available for the time period 1881–1990. Precipitation data measured at 44 different stations for the time period 1977–1990 in the catchment of the river Ruhr (Germany) are used to demonstrate the model.

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Abstract. Hydrological variables and processes usually exhibit a large spatial variability. Often this variability includes aspects of organization and randomness. Because any hydrological modeling has to deal with the question of spatial variability, methods that quantify the effects of spatial variability are valuable. Moreover, it is important to identify the situations where the spatial variability can be reduced (e.g., by using an "effective" value). For a small and well-instrumented catchment in a loess area in southwest Germany effects of spatial variability of the initial soil moisture and soil hydraulic properties on the runoff are investigated. The analysis is performed with a process-oriented rainfall runoff model. It is shown that organization in spatial patterns of soil moisture and soil properties may have a dominant influence on the catchment runoff. The simulations suggest that spatial variability can result in a complex, event dependent, behavior. It cannot be expected that a model with inputs based on mean parameters or mean initial conditions leads to mean outputs for heterogeneous fields. The analysis of different events' shows the changing influence of spatial variability on the runoff with changing storm size. For very small and for large events spatial variability plays a negligible role. A large influence is found for medium-sized events.

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