The reconnaissance drought index (RDI) for the Setsoto municipality of the Free State province in South Africa was calculated for the period between 1985 and 2019 at 3 month (October–December), 6 month (October–March), and 12 month (October–September) intervals. Rainfall and minimum and maximum temperature data from four weather stations (Clocolan, Ficksburg, Marquard, and Senekal) were used for this study to characterize drought using “DrinC” software together with the Mann Kendall test with Sen’s slope to detect drought trends and the rate of change. Extreme, severe, and moderate droughts were recorded for all the stations, with RDIs ranging from −3.6 to −1.0 at different temporal scales. The years 1991, 1994, 2006, 2011, and 2015 were highlighted using the RDI 3, 6, and 12 month calculations. Results showed that the yield decreased either in the year of the drought or in the subsequent year, due to the exact timing of the low-rainfall events in the season and soil moisture storage. Yields were low, on average 2.5 tons ha⁻¹ year⁻¹, with high variability. Optimal growing conditions are essential in the early part of the season, October–December, for maximizing yield; if droughts are experienced at this time then the yield is more greatly impacted than if the droughts occur later in the season. Spatial analysis shows a large variability of drought patterns across the Municipality, over the years, with the 3 month RDI values giving a more detailed picture of this variability than the 6 and 12 month RDI values.

The majority of people in South Africa eat maize, which is grown as a rain-fed crop in the summer rainfall areas of the country, as their staple food. The country is usually food secure except in drought years, which are expected to increase in severity and frequency. This study investigated the impacts of rainfall and minimum and maximum temperatures on maize yield in the Setsoto municipality of the Free State province of South Africa from 1985 to 2016. The variation of the agroclimatic variables, including the Palmer stress diversity index (PSDI), was investigated over the growing period (Oct–Apr) which varied across the four target stations (Clocolan, Senekal, Marquard and Ficksburg). The highest coefficients of variance (CV) recorded for the minimum and maximum temperatures and rainfall were 16.2%, 6.2% and 29% during the growing period. Non-parametric Mann Kendal and Sen’s slope estimator were used for the trend analysis. The result showed significant positive trends in minimum temperature across the stations except for Clocolan where a negative trend of 0.2 to 0.12 °C year⁻¹ was observed. The maximum temperature increased significantly across all the stations by 0.04–0.05 °C year⁻¹ during the growing period. The temperature effects were most noticeable in the months of November and February when leaf initiation and kernel filling occur, respectively. The changes in rainfall were significant only in Ficksburg in the month of January with a value of 2.34 mm year⁻¹. Nevertheless, the rainfall showed a strong positive correlation with yield (r 0.46, p = < 0.05). The overall variation in maize production is explained by the contribution of the agroclimatic parameters; the minimum temperature (R² 0.13–0.152), maximum temperature (R² 0.214–0.432) and rainfall (R² 0.17–0.473) for the growing period across the stations during the study period. The PSDI showed dry years and wet years but with most of the years recording close to normal rainfall. An increase in both the minimum and maximum temperatures over time will have a negative impact on crop yield.

This study investigates the behaviour of subtropical high-pressure systems and the Hadley cell, which affect the weather of South Africa, using the ERA-Interim database and ensemble of 14 global circulation models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Mass stream function was used to represent the Hadley cell. To analyse the behaviour of the subtropical anticyclones, monthly sea level pressure, the 1018 hPa isobar and the maximum isobar in the study area were used. The seasonal variation of the anticyclones and Hadley circulation is consistent with rainfall over South Africa. During austral summer, a less intense, narrow mass stream function, South Atlantic Subtropical Anticyclone and Mascarene High are located more southwards, causing rainfall over the eastern parts of South Africa. During the austral winter, Hadley circulation, as well as the anticyclones, is stronger and located more northwards, causing rainfall over the southern and southwestern parts of South Africa.

The Intergovernmental Panel on Climate Change in its Sixth Assessment Report reported that increases in extreme weather events can already be detected in every region in the world. For eastern southern Africa the report provides evidence of an increasing trend in extreme precipitation events, but more research is needed to understand how this change is manifesting over regions as diverse as South Africa’s eastern escarpment and northern Mozambique. This study evaluates the trends in extreme precipitation over the Enkangala Escarpment, part of South Africa’s eastern escarpment, over the period 1961 to 2021. Daily precipitation data covering 22 stations with fewer than 10% missing values over 61 years were obtained from the South African Weather Service. A total of twelve extreme events precipitation indices were computed using the ClimPACT2 package, and a time series trend analysis was performed using the Trend Free Whitening Mann Kendal test and a field significance test. The results indicate significant increasing trends in nine out of the twelve extreme events indices that were investigated. The three indices which have shown significant decreasing trends include Consecutive Wet days (CWD) at (0.03 day/year total annual rainfall (PRCPTOT) (0.61 mm/decade) and the annual count of days when precipitation ≥ 10 mm (R10mm) of 0.32 days/decade. Indices such Rx1 days, Rx3 days and Rx5 days exhibited increasing trends of 0.2 days/decade each. Other indices with an increasing trend are CDD (3.6 day/decade), R20mm (0.2 days/decade) and R30mm (0.2 days/decade). The R95p (11.2 mm/decade) and R99p (6.0 mm/decade) indices, and Simple Daily Intensity Index(0.4 mm/decade) also exhibit positive trends over the study period. Overall the analysis is indicative of a change in the nature of rainfall over the Enkangala escarpment of South Africa, with annual rainfall totals, the number of consecutive wet days and the frequency of events with smaller thresholds such as 10 mm/day decreasing, but with extreme events of larger thresholds occurring more frequently. That is, when it rains, it tends to rain more intensely than in the past. The use of a field significance test was useful to formalise when relatively small but spatially homogeneous trends detected at separate weather stations yield statistical significance. The findings of the paper are relevant to decision and policy in multiple of socio-economic sectors active in South Africa’s eastern escarpment.

This study explores rainfall variability and trends in the Enkangala Escarpment of South Africa using station data from 1972 to 2022 (51 years). The coefficient of variation (CV) is indicative of pronounced inter-annual variability in seasonal rainfall totals across the region. The trend-free pre-whitening Mann–Kendall (TFPWMK) test and innovative trend analysis (ITA) were used to determine the presence of monotonic trends in the station records, despite the pronounced inter-annual variability in the time series. Sen’s slope estimator was used to quantify the magnitude of the trends. For a given season, the ITA test, in general, allocates local statistical significance to the time series for more stations compared to the TFPWMK test. For winter, spring and summer, there is spatial coherency of decreasing rainfall trends across the Enkangala Escarpment. These trends also exhibit local significance for spring at most stations, and are indicative of less favorable growing conditions for crops during this season. Reduced spring rainfall is likely to also translate to later planting dates (a shorter growing season) and a longer burning season. Trends for autumn are generally weak and lack in local statistical significance or spatial coherency.