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Temporal, spatial or spatio-temporal probabilistic models are frequently usedfor weather forecasting. The D-vine (drawable vine) copula quantile regression(DVQR) is a powerful tool for this application field, as it can automaticallyselect important predictor variables from a large set and is able to modelcomplex nonlinear relationships among them. However, the current DVQR does notalways explicitly and economically allow to account for additional covariateeffects, e.g. temporal or spatio-temporal information. Consequently, we proposean extension of the current DVQR, where we parametrize the bivariate copulas inthe D-vine copula through Kendall's Tau which can be linked to additionalcovariates. The parametrization of the correlation parameter allows generalizedadditive models (GAMs) and spline smoothing to detect potentially hiddencovariate effects. The new method is called GAM-DVQR, and its performance isillustrated in a case study for the postprocessing of 2m surface temperatureforecasts. We investigate a constant as well as a time-dependent Kendall's Tau.The GAM-DVQR models are compared to the benchmark methods Ensemble Model OutputStatistics (EMOS), its gradient-boosted extension (EMOS-GB) and basic DVQR. Theresults indicate that the GAM-DVQR models are able to identify time-dependentcorrelations as well as relevant predictor variables and significantlyoutperform the state-of-the-art methods EMOS and EMOS-GB. Furthermore, theintroduced parameterization allows using a static training period for GAM-DVQR,yielding a more sustainable model estimation in comparison to DVQR using asliding training window. Finally, we give an outlook of further applicationsand extensions of the GAM-DVQR model. To complement this article, our method isaccompanied by an R-package called gamvinereg.

D-Vine GAM Copula based Quantile Regression with Application to Ensemble Postprocessing