The subseasonal-to-seasonal (S2S) predictive timescale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this timescale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a ‘knowledge-value’ gap, where a lack of evidence and awareness of the potential socio-economic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development – demonstrating both skill and utility across sectors – this dialogue can be used to help promote and accelerate the awareness, value and co-generation of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting timescale.

This article compares the sensitivity of IPCC CMIP3-AR4 and CMIP5-AR5 models used on the latest reports from the Intergovernmental Panel on Climate Change (IPCC) in representing the annual average variations (austral summer and autumn) on three regions in Northeastern Brazil (NNEB) for the periods 1979-2000 using the CMAP (Climatology Merged Analysis of Precipitation) data as reference. The three areas of NNEB chosen for this analysis were the semiarid, eastern, and southern regions. The EOF analysis was performed to investigate how the coupled models resolve the temporal variability of the spatial modes in the Tropical Atlantic Sea Surface Temperature (SST), which drives the interannual variations of the rainfall in the Northeastern Brazil. CMIP3-AR4 and CMIP5-AR5 models presented a good representation of the annual cycle of precipitation.Results from correlation and mean absolute error analysis indicate that both CMIP3 and CMIP5 models produce large errors and barely capture the interannual rainfall variance during austral summer and autumn in Northeast Brazil, this features is closely related to the poor representation of the modes of SST variability in the Tropical Atlantic Ocean. For the summer and autumn rainfall projections in the semiarid region, there was no convergence between the CMIP3 and CMIP5 models. During the summer and autumn in the eastern sector, both the CMIP3 and CMIP5 models projected rainfall above the mean for the 2040-2070 period.

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