How to Develop Comprehensive Explanatory and Confirmatory Techniques for the Characteristics of Global Climate Data? Insights From Six Stations of Humidity Data for the Period 1981 to 2022

One of the most important factors that helps to obtain solid estimates and reliable predictions of the climate change variables which are characterized by continuous and non-monotonic changes is the modeling of the variables efficiently. In addition, the process of modeling climate change depends entirely on the characteristics and features of its variables. This aim is proposed due to the clear deficiency in the availability of papers related to the characteristics and features of global climate change variables. In addition, the literature on this issue indicates that the majority of global climate change studies are concerned with modeling data in developed/developing countries on one hand, and on other hand they rarely address the properties of data on climate change variables. Therefore, our work is considered innovative because we will propose a framework for how we can develop comprehensive explanatory and confirmatory analysis for characteristics of global climate data. And we will address global data related to a developing country as an example to illustrate how to conduct a comprehensive and in-depth analysis that can be served as a global guide to this issue. Based on available data of humidity from six stations for the period from 1981 to 2022, the framework was implemented, and the measures, tests, and methods applied in this paper include several descriptive statistics, testing of hypotheses, and visualizations of the data using the Kernel Density Estimation Method. The findings were decisive, unexpected, and confirmed many indicators and assured that the characteristics of any global climate data should be studied in advance in order to get right data modeling.