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.

Austral summer rainfall trends are analysed over South America from observations and simulations of the Coupled Model Intercomparison Project version 5 between 1902 and 2005. Positive trends in southeastern South America (SESA) and negative ones in the southern Andes (SAn) are the most significant observed features. Mean trends obtained from an ensemble of 59 simulations from 14 models for the historical experiment (including both natural and anthropogenic forcings) are able to reproduce those precipitation changes, although weaker than observed. Most of the simulations reproduce the right sign of the precipitation changes at both regions. However, associated uncertainty ranges (due to both inter-model dispersion and internal climate variability) are still large. Mean trends for the historical experiment are statistically distinguishable from those obtained for the natural-forcing-only experiment, which exhibit negligible mean values at both regions. Results allow concluding that the anthropogenic forcing has at least a partial contribution in explaining the precipitation changes observed in both SESA and SAn regions during the last century.

The purpose of this study is to assess the ability of Coupled Model Intercomparison Project 5 (CMIP5) models in reproducing the variability and change of the austral summer precipitation observed in Southeastern South America (SESA) along the 20th century and beginning of the 21st. Models show a reduction in mean precipitation biases and inter‐model dispersion, and a significant improvement in the representation of the leading pattern of precipitation interannual variability (EOF1), in comparison with Coupled Model Intercomparison Project 3 (CMIP3) models. Changes of the EOF1 activity in the present climate, as represented by both, climate model simulations and rainfall gridded datasets, evidence an increase of the frequency of EOF1 positive events (associated with positive precipitation anomalies in SESA and negative ones in the South Atlantic Convergence Zone) and a decrease of the frequency of EOF1 negative events. Nevertheless there are still large uncertainties due to model differences and the internal variability of the climate system. In order to reduce the impact of model uncertainties, an ensemble of the climate simulations that represent better the features associated with EOF1 activity was built, regardless to which model they correspond. The results obtained with this ensemble confirm that largest precipitation trends in SESA are those represented by climate simulations associated with an increase (decrease) of EOF1 positives (negative) events. It was also found that positive precipitation trends in SESA resulted from climate simulations forced by anthropogenic sources are the largest and significantly different from those from simulations forced by natural sources only, which are not significantly different from zero.