In investigating Kenya rainfall variability and its relationship to other climatic elements it has become imperative to analyze the irregularly distributed rainfall events in time. To meet this requirement, this study used a stepwise regression technique. The study seeks to improve existing rainfall monitoring and prediction in Nairobi. Monthly rainfall data was fitted to several mathematical functions. The best mathematical model which best simulated the March-May (MAM) and October -December (OND) seasonal rainfall over the three stations of analysis was chosen using a stepwise regression technique. The value of R-squared for the best fit was computed to show the percentage of rainfall information that is explained by the variation in the independent (time) variable. From the results obtained, the stepwise regression technique selected the fourth degree polynomial as the best fit for analyzing the March-May (MAM) and October -December (OND) seasonal rainfall data set. Solar cycle period of ten (10) years was employed to get the fourth degree polynomial variables. Hence from the study, it can be deducted that the 4th degree polynomial function can be used to predict the peak and the general pattern of seasonal rainfall over Nairobi, with acceptable error values. This information can be used in the planning and management of water resources over Nairobi. The same information can be extended to other areas.

In investigating Kenya rainfall variability and its relationship to other climatic elements it has become imperative to analyze the irregularly distributed rainfall events in time. To meet this requirement, this study used a stepwise regression technique. The study seeks to improve existing rainfall...