Across South Africa, a wide range of activities is influenced by differences in seasonality. In a South African context, there is little consensus on the timing of seasonal boundaries. Inconsistency exists through the use of ad-hoc approaches to define seasonal boundaries across South Africa. In this paper, we present one of the very first uniform statistical classifications of South African seasonal divisions on the basis of daily temperature data. Daily maximum and minimum temperature data were obtained from 35 selected South African Weather Service meteorological stations that had sufficiently complete data sets and homogeneous time series, spanning the period 1980–2015. An Euclidean cluster analysis was performed using Ward’s D method. We found that the majority of the stations can be classified into four distinct seasons, with the remaining 12 stations’ data best classified into three seasons, using Tavg as the classifier. The statistically classified seasonal brackets include summer (October/November/December/ January/February/March), early autumn (April) and late autumn (May), winter (June/July/August), and spring (September). Exploring the boundaries of seasons, the start of summer and end of winter months follow a southwest to northeastwards spatial pattern across the country. Summers start later and winters end later in the southwestern parts of the country, whereas in the northeast, summers start earlier and winters end earlier. Significance: The findings contribute to the common knowledge of seasonality in South Africa. New seasonal divisions in South Africa are proposed.

Extreme cold events (“cold waves”) have disastrous impacts on ecosystem and human health. Evidence shows that these events will still occur under current increasing mean temperatures. Little research has been done on extreme cold events, especially in developing countries such as South Africa. These events pose a significant threat due to the low adaptive capacity, urgent development needs and relatively inadequate infrastructure in South Africa. This study presents annual and seasonal, spatial and temporal trend analyses of extreme cold temperature events for the period 1960–2016. We apply the World Meteorological Organisation Commission for Climatology and Indices Expert Team on Sector‐Specific Climate Indices (ET‐SCI) to South Africa for the first time, with comparison to the World Meteorological Organisation Expert Team on Climate Change Detection (ETCCDI) indices previously used in South Africa. The extreme cold indices are calculated using the RClimDex and ClimPACT, respectively. Trends were calculated using the non‐parametric Mann‐Kendall test, Spearman Rank Correlation Coefficient and Sen's slope estimates. A decreasing trend is found for annual cold spell duration and cold wave frequency, at rates of 0.10 days.day⁻¹ and 0.02 events.day⁻¹, respectively. Seasonally, coldest day temperatures increased in autumn, with increases of 0.02°C.day⁻¹ for the period 1960–2016. Regionally, increasing trends in annual cold spell duration days were evident in stations located in the Western Cape, Eastern Cape, North‐West Province, at a rate of 0.03 days.day⁻¹. Increasing trends in cold waves were observed for stations in Northern Cape, Gauteng, KwaZulu‐Natal and the Eastern Cape Province, at a rate of 0.01 events.day⁻¹. These results contribute to the awareness and recognition of the incidence and duration of cold extreme events in South Africa, seeing that studies suggest that anomalously cold events may persist in a warming world.

Urban land use and land cover (LULC) change can be efficiently monitored with high-resolution satellite products for a variety of purposes, including sustainable planning. These, together with machine learning strategies, have great potential to detect even subtle changes with satisfactory accuracy. In this study, we used PlaneScope Imagery and machine learning strategies (Random Forests, Support Vector Machines, Naïve Bayes and K-Nearest Neighbour) to classify and detect LULC changes over the City of Cape Town between 2016 and 2021. Our results showed that K-Nearest Neighbour outperformed other classifiers by achieving the highest overall classification of accuracy (96.54% with 0.95 kappa), followed by Random Forests (94.8% with 0.92 kappa), Naïve Bayes (93.71% with 0.91 kappa) and Support Vector Machines classifiers with relatively low accuracy values (92.28% with 0.88 kappa). However, the performance of all classifiers was acceptable, exceeding the overall accuracy of more than 90%. Furthermore, the results of change detection from 2016 to 2021 showed that the high-resolution PlanetScope imagery could be used to track changes in LULC over a desired period accurately.

The 6th Assessment of the Intergovernmental Panel on Climate Change projects increasing thermal‐associated morbidity and mortality under anthropogenically induced warming. Over 100 indices exist to quantify thermal stress, and among these, the Universal Thermal Climate Index (UTCI) was developed for regional investigations of thermal stress influences on human health. Although by definition a universal index, current applications are mainly limited to Europe. For regions such as Africa, use of the UTCI has been hampered by a lack of available requisite input variables from ground‐based meteorological stations. To overcome this, a gridded dataset, derived from ERA5 reanalysis, of UTCI equivalent temperatures was developed by the European Centre for Medium‐Range Weather Forecasts. Using this dataset for daily average, minimum and maximum UTCI values, we explore spatiotemporal patterns and changes thereof over annual, seasonal, and monthly scales across southern Africa from 1979 to 2021. Across these scales, 9 of 10 UTCI thermal stress categories were observed, ranging from very strong cold stress to extreme heat stress. Spatially, no thermal stress was most widespread for daily mean values, whereas for daily maximum (minimum) values there was a wider heat (cold) stress incidence, with frequent occurrences of moderate and strong heat stress (slight and moderate cold stress). Interannually, a clear El Niño–Southern Oscillation influence on thermal stress was evident during summer, with El Niño (La Niña) phases extending (reducing) heat stress incidences by up to 13.8% (2.9%). Over the study period, heat stress increased at statistically significant rates in many instances, with the strongest, most widespread increases, for the daily average and maximum (minimum), during spring (summer), averaging 0.28 and 0.29°C·decade⁻¹ (0.23°C·decade⁻¹); few regions experienced statistically significant decreasing trends. Overall, the trend results highlight regions vulnerable to significant thermal climate changes, and thus should be considered in decision‐making regarding outdoor activities.

Consistent with global patterns, heatwaves have increased in frequency, duration and intensity across southern Africa; under enhanced global warming this is projected to worsen. Heatwaves have many adverse impacts, but in biologically unique mountainous regions, like the Maloti-Drakensberg region, impacts on ecological processes and hydrological cycles are particularly dire given their highly sensitive nature. Despite this, extreme temperature changes and interannual variability patterns remain understudied in mountainous regions, owing largely to remoteness and inaccessibility. This is especially true for the Maloti-Drakensberg region. Hence, using heatwave indices developed by the World Meteorological Organization (WMO) Expert Team on Sector-Specific Climate Indices (ET-SCI), we explored spatiotemporal patterns for the seasonal average number, length and magnitude of summer (November-March) heatwaves for 1979/80–2020/21 using the AgERA5 reanalysis. Although higher elevation regions typically experienced more frequent, longer-lasting heatwave events with higher interannual variability levels, on average the Maloti-Drakensberg region experienced 1.4 heatwave events, lasting for 6.5 days with a magnitude of 3.5°C². Interannually, the El Niño-Southern Oscillation influenced variability of heatwave characteristics, with positive (negative) heatwave number and length anomalies predominantly detected during El Niño (La Niña) phases, while for heatwave magnitudes no clear pattern was evident. These heatwave aspects were predominantly characterised by increasing trends, however, few regions exhibited statistically significant trends. Overall, trends averaged 0.003 events/year, 0.03 days/year and 0.02°C²/year for the mean number, length and magnitude of heatwaves, respectively. These results highlight that the Drakensberg-Maloti region is vulnerable to an increasing frequency, duration and magnitude of heatwaves, however, implications thereof require further study.

The rapid growth of cities, owing to rural-urban migration and high birth rate has resulted in encroachment of the land use and land cover (LULC) in the Cape Flats, situated north of Cape Town, Western Cape, South Africa. PlanetScope imagery and K-Nearest Neighbour (KNN) classifier are used to map and monitor the following LULC classes within the Cape Flats; waterbodies, trees, built-up, and vegetation, we further do a time series map between 2016 and 2021. The results showed that LULC changes between 2016 to 2021 are as follows: a decrease of 2.1% in vegetation, 0.4% in water bodies, and 11% in trees. Built-up areas, on the other hand, showed a significant increase of 13.6% over five years. The LULC changes in the Cape Flats were mainly triggered by an increase in built-up areas due to household construction to accommodate the increased population resulting from rural-to-urban migration and high birth rate. Classification accuracy from 2016 and 2021 was as follows; overall accuracy of 98.31% and 0.97 kappa coefficient in 2016, while overall accuracy 96.54% and kappa coefficient 0.95 in 2021. A combination of machine learning and high-resolution imagery showcased that high classification results can be achieved in monitoring subtle LULC changes. We recommended that all relevant stakeholders, including government officials and municipalities, formulate and adopt policies to protect against LULC degradation.