There is a growing need for more systematic, robust, and comprehensive information on the value-add of climate services from both the demand and supply sides. There is a shortage of published value-add assessments that focus on the decision-making context, involve participatory or co-evaluation approaches, avoid over-simplification, and address both the quantitative (e.g., economic) and qualitative (e.g., social) values of climate services. The 12 case studies that formed the basis of the European Union-funded SECLI-FIRM project were co-designed by industrial and research partners in order to address these gaps while focusing on the use of tailored sub-seasonal and seasonal forecasts in the energy and water industries. For eight of these case studies, it was possible to apply quantitative economic valuation methods: econometric modelling was used in five case studies while three case studies used a cost/loss (relative economic value) analysis and avoided costs. The case studies illustrated the challenges in attempting to produce quantitative estimates of the economic value-add of these forecasts. At the same time, many of them highlighted how practical value for users—transcending the actual economic value—can be enhanced; for example, through the provision of climate services as an extension to their current use of weather forecasts and with the visualisation tailored towards the user.

Seasonal climate predictions can assist with timely preparations for extreme episodes, such as dry or wet periods that have associated additional risks of droughts, fires and challenges for water management. Timely warnings for extreme warm summers or cold winters can aid in preparing for increased energy demand. We analyse seasonal forecasts produced by three different methods: (1) a multi-linear statistical forecasting system based on observations only; (2) a non-linear random forest model based on observations only; and (3) process-based dynamical forecast models. The statistical model is an empirical system based on multiple linear regression that is extended to include the trend over the previous 3 months in the predictors, and overfitting is further reduced by using an intermediate multiple linear regression model. This results in a significantly improved El Niño forecast skill, specifically in spring. Also, the Indian Ocean dipole (IOD) index forecast skill shows improvements, specifically in the summer and autumn months. A hybrid multi-model ensemble is constructed by combining the three forecasting methods. The different methods are used to produce seasonal forecasts (three-month means) for near-surface air temperature and monthly accumulated precipitation seasonal forecast with a lead time of one month. We find numerous regions with added value compared with multi-model ensembles based on dynamical models only. For instance, for June, July and August temperatures, added value is observed in extensive parts of both Northern and Southern America, as well as Europe.

In the SADC region of Eastern Africa the onset and duration of the rainy season is of high importance to the agriculture and general water resource management. The planting time, selection of crops and success of different crops is linked to how skillfully this date can be forecasted. As part of the Horizon 2020 project called FOCUS-Africa, in order to forecast this specific onset-date and duration for a specific location in Tanzania, we have constructed a statistical model utilizing the Random Forest algorithm. This is being trained using a mix of observation of past teleconnection indices such as IOD and ENSO3.4 from recent months that from earlier studies have shown to be connected to the onset date and dynamical seasonal forecast of precipitation with a daily temporal resolution. At this stage three dynamical models are included. Finally, the observed precipitation of the previous months is being used as predictors as well. The first results have shown an improvement of the statistical model over using climatic information such as mean onset date as the reference forecast. This can be achieved 2-3 months ahead of the onset date. Furthermore, a relatively large importance of the seasonal forecast systems and the teleconnection indices seems to be present several months ahead of the observed onset date. This also indicates the importance of mixing observations and dynamical models in order to optimize the best possible overall skill of the system in predicting the onset date of the rainy season and thereby supporting local agriculture.

Accurate prediction of the rainy-season onset is crucial for many in Southeast Africa. This paper explores the application of the Random Forest (RF) machine learning method to forecast the start of the October-November-December (OND) rainy season across the region. Our RF model was trained to predict onset dates using historical precipitation data, observations and forecasts of key climate drivers, such as the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), and onset forecasts derived from dynamical seasonal forecast models. Comparing our RF model with the widely used ECMWF System 5.1 dynamical forecast showed improvements of 2-15 days in RMSE, particularly for forecast initialised in August-September, and CRPSS gains of up to +0.15 for individual countries for the same start dates. The results demonstrate that our RF model captures regional variability in onset timing and consistently outperforms climatology. Notably, predictions for the 2023 and 2024 rainy seasons reveal significant shifts driven by ENSO phase transitions. This study highlights the robustness of our RF approach in dealing with complex climate variables and underscores the potential for improving seasonal rainfall forecasts in the region, helping stakeholders make more informed decisions based on these forecasts.