A Vine Copula Framework for Non-Stationarity Detection Between Precipitation and Meteorological Factors and Possible Driving Factors

Increasing climate change leads to the variability of dependencies among meteorological factors. Currently, the investigation of the interdependence of meteorological variables primarily focuses on the bivariate relationships, such as precipitation and temperature or precipitation and wind speed. However, the high-dimensional dependencies among multiple meteorological factors have not been thoroughly explored. This paper proposes a statistical analysis framework that comprehensively analyzes the changes in dependencies among meteorological factors. This statistical analysis framework is based on multivariate joint distributions and enables the detection of dependency change points as well as the analysis of drivers using total probability formulations and orthogonal experiments. Taking the Huang-Huai-Hai region, a recipient area of the South-to-North Water Diversion project, as the study area, we constructed a vine copula-based multivariate joint distribution for precipitation (Pre) and six meteorological factors: temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Win), relative humidity (Rhu), and the Southern Oscillation Index (SOI). The results indicate that a change point exists in the dependence of the 7-dimensional variables (Pre and six meteorological factors) in the Huang-Huai-Hai region in 2013. Tmin, Win, and Tmax are the primary driving factors affecting the precipitation–meteorological dependency relationship. The cumulative distribution function (CDF) is used to describe the probability distribution of precipitation and related meteorological factors. The optimal CDF values of the multivariate joint distribution model were achieved with Rhu and Tmax at level 3, SOI and Tm at level 2, and Win and Tmin at level 1. The results can provide a theoretical method for testing the non-stationarity of high-dimensional meteorological variable dependencies and offer conditional probability support for constructing meteorological prediction machine learning models.