Influence of Temperature Fluctuations on Rainfall Variations Using Statistical and Machine Learning Approaches over Selected Stations in Nigeria Francis Olatunbosun Aweda 1* , Timothy Kayode Samson 2 , Solomon Oluwadara Adeola 3 , Olusanya Odunayo Jegede 1 , Isaac Adewale Ojedokun 4 , Jacob Adebayo Akinpelu 1 1 Physics Programme, College of Agriculture, Engineering and Science, Bowen University, Iwo 540004, Nigeria 2 Statistics Programme, College of Agriculture, Engineering and Science, Bowen University, Iwo 540004, Nigeria 3 Bangor University International College, Oswalds Building, Bangor North Wales LL57 2EN, UK 4 Electrical and Electronics Programme, College of Agriculture, Engineering and Science, Bowen University, Iwo 540004, Nigeria Corresponding Author Email: aweda.francis@bowen.edu.ng Copyright: ©2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). https://doi.org/10.18280/eesrj.110303 ABSTRACT Received: 25 July 2024 Revised: 21 August 2024 Accepted: 3 September 2024 Available online: 19 September 2024 Rainfall and temperature studies were conducted on selected Nigerian stations using data from the HelioClim website archive. This study investigates the relationship between temperature fluctuations and rainfall variations across selected Nigerian stations from 1980 to 2022 using statistical tools and Machine Learning Algorithms (MLA). The used data were analyzed using the Kolmogorov-Smirnov test, Spearman’s rank correlation, and five regression models: linear, logarithmic, inverse, quadratic, and cubic regression models. The models were fitted and their performances were evaluated using R 2 , MSE, and RMSE as performance metrics. Moreover, the data were analyzed with E-view 7.0 and the Statistical Package for Social Sciences (SPSS version 20.0). 20% of the data was tested, while the remaining 80% was trained using 24 MLA. The statistical analysis revealed that the maximum rainfall and temperature for the stations ranged from 200 to 240 mm and 25.8 to 26.8℃. In comparison, the minimum rainfall and temperature ranged from 110 to 140 mm and 24.2 to 24.6℃. The Kolmogorov-Smirnov shows that rainfall was not normally distributed in all locations (p < 0.05) while temperature followed normal distribution in Ibadan (p = 0.182, p > 0.05), Akure (p = 0.200, p > 0.05) and Abeokuta (p = 0.107, p > 0.05). More so, a negative relationship was more pronounced in Ikeja (r = - 0.408, p = 0.000, p < 0.01) and Abeokuta (r = -0.408, p = 0.000, p < 0.01), compared with what was obtained in other locations. The MLA of regression type revealed that temperature has its highest R 2 (1.00) in 15 models while rainfall has its highest in 5 models. As a result, it is demonstrated that temperature affects rainfall. The findings suggest that temperature fluctuations significantly influence rainfall patterns. The research recommended that necessary agencies be required to establish more data collection centres for improved climate monitoring and forecasting. Keywords: atmospheric parameters, meteorology, statistical tools, machine learning algorithms, MERRA-2 1. INTRODUCTION Temperature changes have been identified as a global challenge, with serious consequences for global rainfall distribution over decades, resulting in low water distribution by water distribution companies, most notably in African sub- regions, specifically Nigeria. Temperature and rainfall variability, as reported by Aweda and Samson [1], has become a serious challenge around the world as a result of researcher forecasts and predictions. According to Buishand and Brandsma [2], temperature changes have a significant impact on rainfall variation. According to Intergovernmental Panel on Climate Change (IPCC) [3], diplomatic groups on climate change lead to serious challenges on climate, which pose effects on water reservoirs as well as drought, which mostly affects all areas causing death and serious challenges over various governments at all levels in Africa sub-regions. According to the report, countries such as India have greatly improved the development and management of water resources as important on rainfall over temperature changes in the country, indicating that rainfall is a seasonal occurrence [4]. Aweda et al. [5], on the other hand, report that the government at all levels should create good rules for the utilization of rainfall collected during the period of heavy rainfall for the benefit of farmers and domestic usage during periods of high temperatures, whereby drainage, streams, and rivers may have dried across the country. IPCC [6] stated in the report that there are variations in the rise of temperature in the Indian subcontinent due to changes in the country’s weather conditions. This, however, applies to what has been observed in Nigerian stations, where air temperature has a significant effect on the rainfall observed. Rainfall varies in time with a large amount of value experienced on a monthly, annual, and seasonal basis; however, different authors such as Environmental and Earth Sciences Research Journal Vol. 11, No. 3, September, 2024, pp. 61-71 Journal homepage: http://iieta.org/journals/eesrj 61