22 citations as recorded by crossref.

1. Comparing the S2S hindcast skills to forecast Iran’s precipitation and capturing climate drivers signals over the Middle East H. Ghaedamini et al. https://doi.org/10.1007/s00704-024-04922-w
2. Multi-model Subseasonal Precipitation Forecasts over the Contiguous United States: Skill Assessment and Statistical Postprocessing Y. Li et al. https://doi.org/10.1175/JHM-D-21-0029.1
3. The influence of global climate drivers on monsoon onset variability in Nigeria using S2S models E. Olaniyan et al. https://doi.org/10.1007/s40808-019-00606-x
4. Evaluation of sub-seasonal to seasonal rainfall forecast over Zambia B. Musonda et al. https://doi.org/10.1007/s12040-020-01548-0
5. Predictability of Precipitation and Intraseasonal Variability: Insights From ECMWF Model Skill Over Brazil C. Sapucci et al. https://doi.org/10.1002/joc.8820
6. Subseasonal to seasonal streamflow forecasting in a semiarid watershed P. Broxton et al. https://doi.org/10.1111/1752-1688.13147
7. Evaluation of the ECMWF Sub-seasonal to Seasonal Precipitation Forecasts during the Peak of West Africa Monsoon in Nigeria E. Olaniyan et al. https://doi.org/10.3389/fenvs.2018.00004
8. Sub-seasonal extreme rainfall prediction in the Kelani River basin of Sri Lanka by using self-organizing map classification J. Vuillaume et al. https://doi.org/10.1007/s11069-018-3394-9
9. The influence of antecedent conditions on flood risk in sub-Saharan Africa K. Bischiniotis et al. https://doi.org/10.5194/nhess-18-271-2018
10. Evaluation and Bias Correction of S2S Precipitation for Hydrological Extremes W. Li et al. https://doi.org/10.1175/JHM-D-19-0042.1
11. Flexible forecast value metric suitable for a wide range of decisions: application using probabilistic subseasonal streamflow forecasts R. Laugesen et al. https://doi.org/10.5194/hess-27-873-2023
12. Development of a Statistical Subseasonal Forecast Tool to Predict California Atmospheric Rivers and Precipitation Based on MJO and QBO Activity C. Castellano et al. https://doi.org/10.1029/2022JD037360
13. Different Time-Increment Rainfall Prediction Models: a Machine Learning Approach Using Various Input Scenarios A. Rahimi et al. https://doi.org/10.1007/s11269-024-04040-2
14. Deterministic and probabilistic evaluation of raw and post processed sub-seasonal to seasonal precipitation forecasts in different precipitation regimes R. Kolachian & B. Saghafian https://doi.org/10.1007/s00704-018-2680-5
15. Improving Global Subseasonal to Seasonal Precipitation Forecasts Using a Support Vector Machine‐Based Method G. Yin et al. https://doi.org/10.1029/2023JD038929
16. Performance Evaluation of a Subseasonal to Seasonal Model in Predicting Rainfall Onset Over West Africa N. Kumi et al. https://doi.org/10.1029/2019EA000928
17. The drivers of extreme rainfall event timing in Australia R. Dey et al. https://doi.org/10.1002/joc.7218
18. Community Response and Engagement During Extreme Water Events in Saskatchewan, Canada and Queensland, Australia D. McMartin et al. https://doi.org/10.1007/s00267-017-0944-y
19. Hydrological Outlook UK: an operational streamflow and groundwater level forecasting system at monthly to seasonal time scales C. Prudhomme et al. https://doi.org/10.1080/02626667.2017.1395032
20. Novel evaluation of sub‐seasonal precipitation ensemble forecasts for identifying high‐impact weather events associated with the Indian monsoon S. Kolusu et al. https://doi.org/10.1002/met.2139
21. Evaluation of a BCC-CPSv3-S2Sv2 Model for the Monthly Prediction of Summer Extreme Precipitation in the Yellow River Basin Z. Li et al. https://doi.org/10.3390/atmos16070830
22. The “Last-mile Efforts” of Subseasonal Prediction and Services for Climate Resilience and Sustainability: Review and Outlook J. Yang et al. https://doi.org/10.1007/s00376-025-5256-1