NONE 20260717150234563 wseas/wseas content-registration-form@crossref.org content-registration-form International Journal of Applied Sciences & Development 2945-0454 Lasisi T. A. Department of Statistics, Ladoke Akintola University of Technology, Ogbomoso, NIGERIA Adejumo T. J. Department of Statistics, Ladoke Akintola University of Technology, Ogbomoso, NIGERIA Titilola A. A. Department of Statistics, Olabisi Onabanjo University, Ago-Iwoye, NIGERIA Oshuporu O. A. Department of Statistics, Ladoke Akintola University of Technology, Ogbomoso, NIGERIA Shadare O. O. Department of Statistics, Ladoke Akintola University of Technology, Ogbomoso, NIGERIA Abdulrouf W. A. Department of Statistics, Ladoke Akintola University of Technology, Ogbomoso, NIGERIA Oluwarohunbi G. O. Department of Statistics, Olabisi Onabanjo University, Ago-Iwoye, NIGERIA Oladiran O. O. Department of Statistics, Olabisi Onabanjo University, Ago-Iwoye, NIGERIA In Nigeria, rainfall has a significant impact on land use patterns, agricultural output, and ecological systems. For agriculture, water resource management, and other climate-sensitive industries to make wise decisions, reliable rainfall forecasting is essential. The Nigerian Meteorological Agency (NiMet) provided monthly rainfall data (measured in millimeters) for southern Nigeria from Jan. 2009 to Dec. 2024. This study uses time series techniques to evaluate the data. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and Augmented Dickey–Fuller (ADF) tests were used to evaluate the series' stationarity. The SARIMA(5,0,5)(0,0,1)[12] model offered the greatest fit, according to model identification and selection based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Diagnostic analyses confirmed the adequacy of the model, as the residuals were approximately normally distributed and exhibited no significant autocorrelation. The selected model was subsequently used to forecast for the monthly rainfall from Jan. 2025 to Dec. 2026, with 80% and 90% confidence intervals. The results suggest a relatively stable rainfall pattern characterized by pronounced seasonal variations, highlighting the reliability of the model’s predictive performance. These findings offer valuable insights for agricultural stakeholders, supporting informed crop scheduling, irrigation planning, and disaster risk management. Overall, the study demonstrates the effectiveness of SARIMA-based forecasting as a tool for climate-responsive planning and resilience enhancement in southern Nigeria.” 07 17 2026 07 17 2026 94 11 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232029.2026.5.11 https://wseas.com/journals/asd/2026/a22asd-011(2026).pdf Adejumo, A. O., Oloyede, T. O., Adejumo, O. A., Oguntunde, P. E., Odetunmibi, O. A., Ikoba, N. A., and Job, O. (2018). Modelling of Enugu State monthly rainfall using Box and Jenkins methodology. Annals. Computer Science Series, 16(1), 156-163. Adejuwon, J. O. (2006). Food crop production in Nigeria. Climate Research, 32(2), 145–155. Adejuwon, J. O., & Olaniyan, S. B. (2019). Drought occurrence in the sub-humid eco-climatic zone of Nigeria. Theoretical and Applied Climatology, 137, 1625-1636. https://doi.org/10.1007/s00704-018-2670-7 Adetunji, O., and Olayemi, T., (2021). Influence of data preprocessing on machine learning rainfall forecasting models. African Journal of Environmental Science and Technology, 15(5), 189–198. Adeyeri, O. E., Lamptey, B. L., Lawin, A. E., and Sanda, I. S. (2017). Spatio-temporal precipitation trend and homogeneity analysis in KomaduguYobe basin, Lake Chad region. Journal of Climatology & Weather Forecasting, 5(3), 1000214. Akinsanola, A. A., and Ogunjobi, K. O. (2014). Analysis of rainfall and temperature variability over Nigeria. Global Journal of Human-Social Science: B Geography, Geo-Sciences, Environmental Disaster Management, 14(3), 1-17. Aliyu, A. S., Auwal, A. M., & Adenomon, M. O. (2021). Application of SARIMA Models in Modelling and Forecasting Monthly Rainfall in Nigeria. Asian Journal of Probability and Statistics, 13(3), 30-43. https://doi.org/10.9734/ajpas/2021/v13i330310 Bandara, K., Hyndman, R. J., and Bergmeir, C. (2025). MSTL: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns. International Journal of Operational Research, 52(1), 79-98. Belina, Y., and Kebede, A. (2023). Comparative study of Artificial Neural Network (ANN) and Support Vector Regression (SVR) in rainfall-runoff modeling of Awash Belo Watershed, Awash River Basin, Ethiopia. Datti, A. D., Zeng, G., Tarnavsky, E., Cornforth, R., Pappenberger, F., Abdullahi, B. A., and Onyejuruwa, A. (2024). Evaluation of satellitebased rainfall estimates against rain gauge observations across agro-climatic zones of Nigeria, West Africa. Remote Sensing, 16(10), 1755. Ghile, Y., Schulze, R., and Brown, C. (2010). Evaluating the performance of ground-based and remotely sensed near real-time rainfall fields from a hydrological perspective. Hydrological Sciences Journal–Journal des Sciences Hydrologiques, 55(4), 497-511. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). Melbourne: OTexts. https://otexts.com/fpp3/ Ibrahim, A., & Musa, A. O. (2023). On the performance of SARIMA and SARIMAX model in forecasting monthly average rainfall in Kogi State, Nigeria. FUDMA Journal of Sciences, 7(6), 24-31. https://doi.org/10.33003/fjs-2023-0706-2095. Ikpang, I. N., Okon, E. O. J., and George, E. U. (2022). Modeling Average Rainfall in Nigeria With Artificial Neural Network (ANN) Models and Seasonal Autoregressive Integrated Moving Average (SARIMA) Models. International Journal of Statistics and Probability, 11(4), 53-62. Jajarmizadeh, M., Sidek, L. M., Mirzai, M., Alaghmand, S., Harun, S., and Majid, M. R. (2016). Prediction of surface flow by forcing of climate forecast system reanalysis data. Water resources management, 30(8), 2627-2640. Mirzaei H. A., Erfanian, M., Javan, K., and Najafi, M. R. (2024). Daily precipitation concentration and Shannon's entropy characteristics: spatial and temporal variability in Iran, 1966– 2018. Theoretical & Applied Climatology, 155(1). Mishra R. K. and Dubey S.C. (2023). Fresh water availability and its global challenge. Journal of Marine Science and Research, 4(3), 1-78. Olatayo, T. O., Adejumo, T. J., Rasaki, Y. A., Lasisi, T. A., and Abdulrouf, W. A. (2025). Predicting the average price of some selected food items in Nigeria using time series analysis. Quality & Quantity, 1-14. Doi.org/10.1007/s11135-025- 02334-0. Oguntunde, P. G., Abiodun, B. J., and Lischeid, G. (2011). Rainfall trends in Nigeria, 1901– 2000. Journal of Hydrology, 411(3-4), 207-218. Robinson, W. A. (2021). Climate change and extreme weather: A review focusing on the continental United States. Journal of the Air & Waste Management Association, 71(10), 1186- 1209. Singh, P., Sharma, A., and Dhankhar, J. (2022). Climate change and soil fertility. Journal of Animal Science and Biotechnology. (pp. 25-59). DOI: 10.1007/978-981-16-7759-5_3 Spiridonov, V., and Ćurić, M. (2020). Clouds and precipitation. In Fundamentals of meteorology (pp. 135-158). Cham: Springer International Publishing. DOI: 10.1007/978-3-030-52655-9_11 Trenberth, K. E. (2018). Climate change caused by human activities is happening and it already has major consequences. Journal of energy and natural resources law, 36(4), 463-481. doi.org/10.1080/02646811.2018.1450895 Upadhyay, R. K. (2020). Markers for global climate change and its impact on social, biological and ecological systems: A review. American Journal of Climate Change. Volume 9, Issue 3, Pages 159–203 DOI: 10.4236/ajcc.2020.93012 Valipour, M. (2015). Long‐ term runoff study using SARIMA and ARIMA models in the United States. Meteorological Applications, 22(3), 592- 598. doi.org/10.1002/met.1491 Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control (Revised ed.). San Francisco, CA: Holden-Day.