This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman’s correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer—December, January, and February (DJF)—can be explained by spring—September, October, and November (SON)—rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.

Motivated by the risks posed by global warming, historical trends and future projections of near-surface temperature in South Africa have been investigated in a number of previous studies. These studies included the assessment of trends in average temperatures as well as extremes. In this study, historical trends in near-surface minimum and maximum temperatures as well as extreme temperature indices in South Africa were critically investigated by comparing quality-controlled station observations with downscaled model projections. Because climate models are the only means of generating future global warming projections, this critical point comparison between observed and downscaled model simulated time series can provide valuable information regarding the interpretation of model-generated projections. Over the historical 1951–2005 period, both observed data and downscaled model projections were compared at 22 point locations in South Africa. An analysis of model projection trends was conducted over the period 2006–2095. The results from the historical analysis show that model outputs tend to simulate the historical trends well for annual means of daily maximum and minimum temperatures. However, noteworthy discrepancies exist in the assessment of temperature extremes. While both the historical model simulations and observations show a general warming trend in the extreme indices, the observational data show appreciably more spatial and temporal variability. On the other hand, model projections for the period 2006–2095 show that for the medium-to-low concentration Representative Concentration Pathway (RCP) 4.5, the projected decrease in cold nights is not as strong as is the case for the historically observed trends. However, the upward trends in warm nights for both the RCP4.5 and the high concentration RCP8.5 pathways are noticeably stronger than the historically observed trends. For cool days, future projections are comparable to the historically observed trends, but for hot days noticeably higher. Decreases in cold spells and increases in warm spells are expected to continue in future, with relatively strong positive trends on a regional basis. It is shown that projected trends are not expected to be constant into the future, in particular trends generated from the RCP8.5 pathway that show a strong increase in warming towards the end of the projection period. Significance: Comparison between the observed and simulated trends emphasises the necessity to assess the reliability of the output of climate models which have a bearing on the credibility of projections. The limitation of the models to adequately simulate the climate extremes, renders the projections conservative.

Forecast skill of three subseasonal‐to‐seasonal models and their ensemble mean outputs are evaluated in predicting the surface minimum and maximum temperatures at subseasonal timescales over South Africa. Three skill scores (correlation of anomaly, root‐mean‐square error, and Taylor diagrams) are used to evaluate the models. It is established that the subseasonal‐to‐seasonal models considered here have skill in predicting both minimum and maximum temperatures at subseasonal timescales. The correlation of anomaly indicates that the multimodel ensemble outperforms the individual models in predicting both minimum and maximum temperatures for the day 1–14, day 11–30, and full calendar month timescales during December months. The Taylor diagrams suggest that the European Centre for Medium‐Range Weather Forecasts model and MM performs better for the day 11–30 timescale for both minimum and maximum temperatures. In general, the models perform better for minimum than maximum temperatures in terms of root‐mean‐square error. In fact, the skill difference in terms of correlation of anomalies (CORA) is small.

This research study evaluated the projected future climate and anticipated impacts on water-linked sectors on the transboundary Limpopo River Basin (LRB) with a focus on South Africa. Streamflow was simulated from two CORDEX-Africa regional climate models (RCMs) forced by the 5th phase of the Coupled Model Inter-Comparison Project (CMIP5) Global Climate Models (GCMs), namely, the CanESM2m and IPSL-CM5A-MR climate models. Three climate projection time intervals were considered spanning from 2006 to 2099 and delineated as follows: current climatology (2006–2035), near future (2036–2065) and end of century future projection (2070–2099). Statistical metrics derived from the projected streamflow were used to assess the impacts of the changing climate on water-linked sectors. These metrics included streamflow trends, low and high flow quantile probabilities, the Standardized Streamflow Index (SSI) trends and the proportion (%) of dry and wet years, as well as drought monitoring indicators. Based on the Mann-Kendall (MK) trend test, the LRB is projected to experience reduced streamflow in both the near and the distant future. The basin is projected to experience frequent dry and wet conditions that can translate to drought and flash floods, respectively. In particular, a high proportion of dry and a few incidences of wet years are expected in the basin in the future. In general, the findings of this research study will inform and enhance climate change adaptation and mitigation policy decisions and implementation thereof, to sustain the livelihoods of vulnerable communities.

Background: Malaria, though curable, continues to be a major health and socioeconomic challenge. Malaria cases have been on the rise for the last two years in the malaria-endemic region of South Africa. Thulamela Municipality in Limpopo, South Africa, which falls within several municipalities at Vhembe district that are affected by malaria. About 33,448 malaria cases were reported over a period of 20 years (1998 January-2018 December). Objective: The study aims to determine the influence of climate on the spatiotemporal distribution of malaria cases in Thulamela Municipality for the last two decades (1998 January-2018 December). Methods: The analysis is divided into two sections, including temporal and spatial distribution of malaria cases, and the correlating climatic and environmental factors. Time series analysis is conducted to determine the variations of malaria and climate. Malaria and climatic factors (rainfall, maximum temperature, minimum temperature) were globally correlated using matrix scatterplot spearman correlation with a certain significance level. The Ordinary Least Squares (OLS) regression was performed to determine the significant climate factors that locally affect the spatial distribution of malaria cases. The local environmental factor (rivers) was analyzed using buffering and terrain analysis. Results: A positive spearman correlation of the time series was found with the significance level of 0.01. The climate variables were not strongly significant to the spatial distribution of malaria at the village level. The villages which continued to record high malaria cases were in proximity to rivers by 2km. The Thulamela municipality falls within 20-30°C, which is essential for the incubation of mosquitoes and transmission of malaria. The areas receiving about 125 to 135 mm of total monthly rainfall record high malaria cases. The temperature, rainfall, and rivers are important factors for malaria transmission. Conclusion: Knowledge of the drivers of the spatiotemporal distribution of malaria is essential for a predicting system to enhance effective malaria control in communities such as the Thulamela municipality.