This study assesses the climate change impact on rainfall and drought incidents across Nigeria. Linear regression, Mann-Kendall tests and lag-1 serial correlation were adopted to analyse the trends and variability of rainfall and drought at 18 synoptic stations. Analysis of annual precipitation series indicates an increase in rainfall amounts at all stations, except Minna, Gusau and Yola. Seventeen of the 18 stations recorded at least one main drought period, between 1983 and 1987. A decreasing trend for the standardized precipitation index SPI-12 series was seen at Yola station, while the other stations showed an increasing trend. Also, Nigeria witnessed more annual rainfall totals but with high variability within the rainy months of the year in the first 15 years of the 21st century compared to the 20th century. Such variability in rainfall may have a significant effect on groundwater resources and the hydrology of Nigeria.

This study examines trends in precipitation (PRE), maximum temperature (TMAX), minimum temperature (TMIN), and potential evapotranspiration (PET) using the Modified Mann-Kendall test and Sen’s slope estimator between 1901 and 2022 in the arid lands of Central Asia, West Asia and North Africa. The results reveal complex spatial and temporal climate change patterns across the study area. Annual PRE shows a slight negative trend (Z = −0.881, p = 0.378), with significant decreases from 1951–2000 (Z = −3.329, p = 0.001). The temperatures exhibit strong warming trends (TMIN: Z = 9.591, p < 0.001; TMAX: Z = 8.405, p < 0.001). PET increased significantly (Z = 6.041, p < 0.001), with acceleration in recent decades. Spatially, precipitation decreased by 10% in maximum annual values, while PET increased by 10–15% in many areas. Temperature increases of 2–3 °C were observed, with TMAX rising from 36–39 °C to 39–42 °C in some MENA regions. Seasonal analysis shows winter precipitation decreasing significantly in recent years (Z = −1.974, p = 0.048), while summer PET shows the strongest increasing trend (Z = 5.647, p < 0.001). Spatial analysis revealed clear latitudinal gradients in temperature and PET, with higher values in southern regions. PRE patterns were more complex, with coastal and mountainous areas receiving more precipitation. The combination of rising temperatures, increasing PET, and variable PRE trends suggest an overall intensification of aridity in many parts of the region. This analysis provides crucial insights into the climate variability of these water-scarce areas, emphasizing the need for targeted adaptation strategies in water resource management, agriculture, and ecosystem conservation.

Data from historical observatories and future simulations were analyzed using the representative concentration pathway (RCP) 8.5 scenario, which covered the period from 1951 to 2100. In order to characterize the drought, three widely used drought indicators were used: the standardized precipitation index (SPI), the reconnaissance drought index (RDI), and the standardized precipitation and evapotranspiration index (SPEI). The ensemble of the seven (7) GCMs that used RCA-4 was able to capture several useful characteristics of Nigeria’s historical climatology. Future climates were forecasted to be wetter than previous periods during the study period based on the output of drought characteristics as determined by SPI. SPEI and RDI predicted drier weather, in contrast. SPEI and RDI’s predictions must have been based on the effect of rising temperatures brought on by global warming as depicted by RCP 8.5, which would then have an impact on the rate of evapotranspiration. According to drought studies using the RCP 8.5 scenario, rising temperatures will probably cause more severe/extreme droughts to occur more frequently. SPEI drought frequency changes in Nigeria often range from 0.75 (2031–2060) to 1.80 (2071–2100) month/year, whereas RDI changes typically range from 0.30 (2031–2060) to 0.60 (2071–2100) month/year. The frequency of drought incidence has recently increased and is now harder to forecast. Since the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) and the Sustainable Development Goals (SDGs) have few more years left to be completed, drastic efforts must be made to create climate-resilient systems that can tackle the effects that climate change may have on the water resources and agricultural sectors.

The impact of climate change on the agricultural and water resources sectors cannot be overemphasized since West Africa is prone to drought as a result of high variability in rainfall and temperature. In this study, an assessment on the impact of climate change in the Sahel Region of Nigeria (SRN) on drought was carried out between 1971 and 2060. Gridded temperature and rainfall datasets from Climate Research Unit (CRU) and seven Global Climate Models (GCMs) using regional climate model (RCA-4) under Representative Concentration Pathway (RCP) 8.5 scenario were used. Standardized Precipitation and Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) at 3- and 12-month time steps were used for drought characterization. The RCA-4 ensemble of the 7 GCMs was able to pattern the seasonal cycle of the maximum and minimum temperatures of SRN well as most of the climatic features were captured as indicated by the CRU observation. Furthermore, the results indicated by the RCA-4 ensemble median of SPEI-3 and SPEI-12 show more drought intensity changes than SPI-3 and SPI-12 as the twenty-first century progresses. An awareness that can help policymakers know the impact climate change may have on the agricultural and hydrological sectors of SRN was created.

The necessity to perform an accurate prediction of future characteristics of drought requires a robust and efficient technique that can deduce from historical data the stochastic relationship or dependency between history and future. In this study, the applicability of the artificial neural network (ANN) is used for forecasting the standardized precipitation and evapotranspiration index (SPEI) at 12‐month timescale for five candidate stations in Nigeria using predictive variable data from 1985 to 2008 (training) and tested data between 2009 and 2015. The predictive variables are monthly average precipitation, average air temperature, maximum temperature, minimum temperature, mean speed, mean solar radiation, sunshine hours, and two large‐scale climate indices (Southern Oscillation Index and North Atlantic Oscillation). From the several combinations of the input variables, training algorithms, hidden, and output transfer functions, a total of eight model runs stood out using a three‐layer ANN network. The most efficient ANN model architecture had 9,8,1 as the input, hidden, and output neurons, respectively, trained using the Levenberg‐Marquardt training algorithm and tansig as the activation and hidden transfer functions. Assessment on the efficiency of the model based on statistics indicate that the coefficient of determination, root mean square error, Nash‐Sutcliffe coefficient of efficiency and the mean absolute error ranges between 0.51 and 0.82; 0.57 and 0.75; 0.28 and 0.79; 0.44 and 0.56, respectively, during the testing period. The output of these findings shows that ANN modeling technique can play a significant role as a data‐driven model in forecasting monthly SPEI time series and drought characteristics in the study area, thereby leading to the development of an early warning system for the country.