  The probability and structure of co-occurrences of extreme values in
multivariate data may critically depend on auxiliary information provided by
covariates. In this contribution, we develop a flexible generalized additive
modeling framework based on high threshold exceedances for estimating
covariate-dependent joint tail characteristics for regimes of asymptotic
dependence and asymptotic independence. The framework is based on suitably
defined marginal pretransformations and projections of the random vector along
the directions of the unit simplex, which lead to convenient univariate
representations of multivariate exceedances based on the exponential
distribution. Good performance of our estimators of a nonparametrically
designed influence of covariates on extremal coefficients and tail dependence
coefficients are shown through a simulation study. We illustrate the usefulness
of our modeling framework on a large dataset of nitrogen dioxide measurements
recorded in France between 1999 and 2012, where we use the generalized additive
framework for modeling marginal distributions and tail dependence in monthly
maxima. Our results imply asymptotic independence of data observed at different
stations, and we find that the estimated coefficients of tail dependence
decrease as a function of spatial distance and show distinct patterns for
different years and for different types of stations (traffic vs. background).
