The issue of climate change is receiving increased public attention. It has a great potential impact on the natural environment and on socioeconomic systems. A lot of current scientific research concerns building models to detect and attribute climate change. As the major input to climate models, daily climate observations taken at point locations are often limited in spatial coverage, incomplete and short in length. Simulating and interpolating multi-site daily precipitation series is a challenging task in view of their discrete-continuous mixed margins.;A copula-based approach is developed to address the problem of multivariate data modeling given autocorrelated discrete-continuous mixture margins. Copulas generated from the elliptical family are compared. Studies on real and simulated precipitation data show that the proposed methodology performs very well in capturing the spatial dependence of the aucorrelated discrete-continuous mixture series. A multivariate inversion method is proposed, aiming at generating truncated or bounded multivariate random vectors with a known cumulative distribution function.
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