Exploring Climate Variability in Lahore, Pakistan: Investigating the Interplay of Precipitation, Relative Humidity, and Temperature

Abstract

In recent years, the rapid changes in climate parameters such as temperature, precipitation, and relative humidity have become increasingly evident. To address this, the study investigates the intricate fluctuations within distinct climatic parameters precipitation, relative humidity, and maximum temperature—for ten years that capture pronounced pre-2020 climatic variability and provides a critical baseline for interpreting more recent and ongoing climatic shifts in Lahore. Understanding these dynamics is important due to Lahore’s vulnerability to climate variation and its implications for agriculture and urban sustainability. Monthly climatic data was investigated to identify trends, while linear regression and correlation analyses were employed to quantify relationships among the variables. Findings revealed that precipitation shows a consistent pattern of minor fluctuations across the years. Of particular note, 2015 emerges as the driest year among the dataset, while 2013 and 2017 stand out as the wettest years. The years 2010 and 2014 observed the highest levels of precipitation. Relative humidity, uncovers a remarkable uniformity in its yearly evolution from 2010 to 2019 having lowest values during the summer months, primarily in May and peak values during the winter months, specifically in January and February. The analysis of maximum temperature patterns, except for 2014, showcases a consistent pattern of uniformity from 2010 to 2019. May consistently records the highest maximum temperatures, while January consistently registers the lowest minimum temperatures. Quantitative analysis demonstrated a strong inverse relationship between maximum temperature and relative humidity, with correlation coefficients ranging from −0.43 to −0.71 and regression coefficients varying from −0.40 to −0.84. Similarly, temperature and precipitation exhibited weak to moderate negative correlations (CRC = −0.14 to −0.32), indicating reduced precipitation under warmer conditions. In contrast, precipitation and relative humidity showed positive associations with correlation coefficients ranging from 0.10 to 0.26 and regression coefficients reaching 0.70, suggesting that increased precipitation contributes to elevated humidity levels. These findings highlight the strong relation among climatic variables and underscore their relevance for agricultural planning, water resource management, and urban climate adaptation strategies particularly in light of intensifying climate variability observed in the post-2019 period.