Research Article A Novel Appraisal Protocol for Spatiotemporal Patterns of Rainfall by Reconnaissance the Precipitation Concentration Index (PCI) with Global Warming Context Zara Tehreem, 1 Zulfiqar Ali, 2 Nadhir Al-Ansari , 3 Rizwan Niaz , 1 Ijaz Hussain , 1 and Saad Sh. Sammen 4 1 Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan 2 College of Statistical and Actuarial Sciences, University of Punjab, Lahore, Pakistan 3 Civil Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea 971 87, Sweden 4 Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah, Diyala Governorate, Iraq Correspondence should be addressed to Nadhir Al-Ansari; nadhir.alansari@ltu.se and Ijaz Hussain; ijaz@qau.edu.pk Received 6 May 2022; Revised 3 June 2022; Accepted 7 July 2022; Published 4 August 2022 Academic Editor: Tahir Mehmood Copyright © 2022 Zara Tehreem et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In global warming contexts, continuous increment in temperature triggers several environmental, economic, and ecological challenges. Its impacts have severe effects on energy, agriculture, and socioeconomic structure. Moreover, the strong correlation between temperature and dynamic changing of rainfall patterns greatly influences the natural cycles of water resources. erefore, it is necessary to examine the spatiotemporal variation of precipitation to improve precipitation monitoring systems. ereby, it helps to make future planning for flood control and water resource management. Considering the importance of the spatio- temporal assessment of precipitation, the current study provides a new method: regional contextual precipitation concentration index (RCPCI) to analyze spatial-temporal patterns of annual rainfall intensities by reconnaissance the precipitation concen- tration index (PCI) in the global warming context. e current study modifies the existing version of PCI by propagating the role of temperature as auxiliary information. Further, based on spatial and nonspatial correlation analysis, the current study compares the performance of RCPCI and PCI for 45 meteorological stations of Pakistan. Tjøstheim’s coefficient and the modified t-test are used for testing and estimating the spatial correlation between both indices. In addition, the Poisson log-normal spatial model is used to assess the spatial distribution of each rainfall pattern. Outcomes associated with the current analysis show that the proposed method is a good and efficient substitute for PCI in the global warming scenario in the presence of temperature data. erefore, to make accurate and precise climate and precipitation mitigation policies, the proposed method may incorporate uncovering the yearly pattern of rainfall. 1. Introduction Climate variability is a change in the statistical distribution of weather patterns. e climate of a region is defined by assessing the variation in humidity, temperature, wind, atmospheric pressure, and precipitation. In recent decades, climatological changes, the temperature, variation in pre- cipitation, increase in the intensity of some extreme weather phenomena, rise in sea levels, etc., negatively impact the natural and human systems worldwide [1]. Certain climatic variables like temperature, precipitation, wind pattern, and solar radiation mainly describe the distinctive features of the climatic system [2]; however, the precipitation and tem- perature are the most significant and conventional climatic variables which change in time and space and affect hy- drological cycle, irrigation schedules, agriculture, and other human activities. Additionally, the change in the intensity and amount of rainfall may lead to extraordinary weather events (e.g., floods, rainstorms, and droughts) [3, 4], which can cause economic and environmental damages and in- creased in mortality rate [5, 6]. However, by considering these issues, the understanding of the spatiotemporal Hindawi Mathematical Problems in Engineering Volume 2022, Article ID 3012100, 9 pages https://doi.org/10.1155/2022/3012100