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.