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We introduce an extension of R-vine copula models for the purpose of spatialdependency modeling and model based prediction at unobserved locations. Thenewly derived spatial R-vine model combines the flexibility of vine copulaswith the classical geostatistical idea of modeling spatial dependencies bymeans of the distances between the variable locations. In particular the modelis able to capture non-Gaussian spatial dependencies. For the purpose of modeldevelopment and as an illustration we consider daily mean temperature dataobserved at 54 monitoring stations in Germany. We identify a relationshipbetween the vine copula parameters and the station distances and exploit it inorder to reduce the huge number of parameters needed to parametrize a54-dimensional R-vine model needed to fit the data. The new distance basedmodel parametrization results in a distinct reduction in the number ofparameters and makes parameter estimation and prediction at unobservedlocations feasible. The prediction capabilities are validated using adequatescoring techniques, showing a better performance of the spatial R-vine copulamodel compared to a Gaussian spatial model.

R-vine Models for Spatial Time Series with an Application to Daily Mean Temperature