Investigation of variability in monthly minimum and maximum temperature with trend methods in Khyber Pakhtunkhwa, Pakistan

Global warming is an inherent phenomenon that has a substantial impact on both the ecosphere and the human population. Understanding and strategizing to mitigate the effects of global warming is of utmost importance. Extensive research regarding climatological factors has been conducted in a recent timeframe. The predominant approach employed in such investigations is trend analysis. This study employs the Trend Polygon Star Concept, Three-Dimensional Innovative Trend Analysis (3D-ITA), and Innovative Polygon Trend Analysis (IPTA) due to their effectiveness in visualizing, analyzing, and understanding complex temperature data in the context of climate change. The IPTA Process is a method used to compare the raw and processed data sets of two data series. To illustrate the test’s findings, the Trend Polygon Star Concept splits the two-month data set interval on a graph—the IPTA output—into four parts. Thus, this research assesses monthly minimum and maximum temperature data using this two-polygon methodology. This data capture spans four decades (1981–2020). As an outcome of the work, polygon graphics were generated. Furthermore, the IPTA method has been used to compute the trend slopes and lengths of temperature data. A list was prepared to provide all the values for the x- and y-axis of graphs created using the Trend Polygon Star Concept Method. The findings of both research methodologies were reviewed for a particular station. Additionally, when the arithmetic mean analysis of monthly maximum temperatures was examined, a rising trend was detected in most months. In contrast, the lowest temperature series revealed no movement in most of the months. When the standard deviation graph was studied, it was discovered that all ten zones had transitions between two months. Using the 3D-ITA strategy, 40% of the entire region was found to have stable trends, whereas 60% of the region examined had unstable trends.