Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

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This paper evaluates sub-seasonal precipitation forecasts for Africa using hindcasts from three models (ECMWF, UKMO, and NCEP) participating in the Subseasonal to Seasonal (S2S) prediction project. A variety of verification metrics are employed to assess weekly precipitation forecast quality at lead times of one to four weeks ahead (Weeks 1-4) during different seasons. Overall, forecast evaluation indicates more skilful predictions for ECMWF over other models and for East Africa over other regions. Deterministic forecasts show substantial skill reduction in Weeks 3-4 linked to lower association and larger underestimation of predicted variance compared to Weeks 1-2. Tercile-based probabilistic forecasts reveal similar characteristics for extreme categories and low quality in the near-normal category. Although discrimination is low in Weeks 3-4, probabilistic forecasts still have reasonable skill, especially in wet regions during particular rainy seasons. Forecasts are found to be over-confident for all weeks, indicating the need to apply calibration for more reliable predictions. Forecast quality within the ECMWF model is also linked to the strength of climate drivers’ teleconnections, namely El Niño-Southern Oscillation, Indian Ocean Dipole, and the Madden-Julian Oscillation. The impact of removing all driver-related precipitation regression patterns from observations and hindcasts shows reduction of forecast quality compared to including all drivers’ signals, with more robust effects in regions where the driver strongly relates to precipitation variability. Calibrating forecasts by adding observed regression patterns to hindcasts provides improved forecast associations particularly linked to the Madden-Julian Oscillation. Results from this study can be used to guide decision-makers and forecasters in disseminating valuable forecasting information for different societal activities in Africa.