Background: Rainfall is the main source of water on the earth’s surface. It infiltrates and percolates deep into the soil for groundwater recharge. Rainfall patterns, amounts, durations, and intensities can vary daily, monthly, annually, and spatially. It is therefore important to accurately estimate rainfall return periods, which can be employed in hydraulic design and flood control measures. Methods: This research considered the survival analysis approach for the prediction of rainfall return periods including intensity, and months during which these would occur in south-western Nigeria. Twenty years’ of annual rainfall data were obtained from three metrological stations and these were subjected to nine different probability plotting position methods.  Results from the plotting positions was further subjected to four  survival models using five years of censor time. The Akaike Information Criterion (AIC) was used to determine the best-fitting model for the dataset. Results: The Laplace probability plotting position in conjunction with the log-logistic distribution best describes the datasets, since it gave the lowest AIC value of 22.53. The log-logistic distribution is also suitable for the prediction of return period from the Weibull probability plotting position since the AIC values were 6.934 and -4.332 respectively.  The Hirsh plotting position in conjunction with the Weibull distribution is also suitable for the description of the dataset. Conclusion: The established parametric models are suitable for the accurate prediction of return periods of peak rainfall events during any month of the year.

Rainfall intensity prediction or forecast is vital in designing hydraulic structures and flood and erosion control structures. In this work, meteorological data were obtained from the National Aeronautics and Space Administration’s (NASA) website. Models estimating maximum rainfall intensities were derived, and some meteorological factors’ effects on the models were tested. The meteorological factors considered include annual relative humidity averages, specific humidity, temperature range at 2 m, maximum temperature, and minimum temperature. This research was aimed at developing a model for estimating maximum rainfall intensities, and the effects of various meteorological factors on the models were investigated. The exponentiated standardized half logistic distribution (ESLD) was used to model the effects of the factors and return periods on 35 years’ (1984–2018) annual maxima monthly rainfall intensities for Port Harcourt metropolis, Nigeria. The model parameters were estimated using the maximum likelihood estimation method. Compared with the results from the five standard distributions, three criteria were used to determine the best-performed distribution. These indicated that the ESLD performed considerably better than the other five compared distributions. Only the return period had significant effects on the model for the rainfall intensity prediction since p < 0.05 , while the effects of the meteorological factors are insignificant.