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.

Land use optimization is essential for balancing the economic development, ecological protection, and sustainable management of regional landscapes. However, most existing frameworks focus primarily on land use area allocation while neglecting spatial pattern metrics, leading to fragmented landscapes and reduced ecological benefits. In this study, we propose a new multi-objective optimization framework that incorporates land use patch number as an explicit objective, providing a more direct way to make a trade-off between economic development, ecological protection, and landscape sustainability. Using Jinan’s Xiaoqing River Basin as a case study, we compare four scenarios, natural development, economic priority, ecological priority, and sustainable development, under both the traditional optimization framework (multi-objective allocation based on area and post-evaluation of spatial pattern based on land use simulation) and the proposed optimization framework. The results show that while the two frameworks yield similar levels of overall economic and ecological benefits, the traditional framework produces highly fragmented ecological land and overly aggregated construction land. In contrast, the new framework effectively reduces cropland and grassland fragmentation and enhances ecological land cohesion, generating more natural and sustainable landscape configurations. These findings highlight the importance of integrating spatial pattern objectives into land use optimization, providing a more comprehensive and spatially explicit approach to land use optimization, offering scientific support for rational land allocation and sustainable development within the urban river basin.