13 citations as recorded by crossref.

1. Impact‐Based Skill Evaluation of Seasonal Precipitation Forecasts Z. Nikraftar et al. https://doi.org/10.1029/2024EF004936
2. Long and short-term perspectives on space–time landslide modelling T. Wang et al. https://doi.org/10.1016/j.jag.2025.104694
3. Evaluation of agro-hydrological subseasonal-to-seasonal forecasts for tactical decision making in crop irrigation E. Fallah-Mehdipour & J. Dietrich https://doi.org/10.1016/j.agwat.2026.110315
4. 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
5. Short-Term Probabilistic Forecasting of Irrigation Need Based on Relative Soil-Moisture-Deficit Persistence Using Copula Models Z. Ma et al. https://doi.org/10.3390/agriculture16212421
6. Improving subseasonal forecast of precipitation in Europe by combining a stochastic weather generator with dynamical models M. Krouma et al. https://doi.org/10.1002/qj.4733
7. Linking the subseasonal variability of the East Asia winter monsoon and the Madden-Julian Oscillation through wave disturbances along the subtropical jet J. Xiu et al. https://doi.org/10.1038/s41612-025-01076-y
8. Proper scoring rules for multivariate probabilistic forecasts based on aggregation and transformation R. Pic et al. https://doi.org/10.5194/ascmo-11-23-2025
9. Water vapor and energy sources of persistent extreme heavy rainfall related to typhoon over the Huaihe River basin J. Yu et al. https://doi.org/10.1016/j.ejrh.2026.104108
10. Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
11. Ensemble post-processing of sub-seasonal to seasonal precipitation forecasts based on a novel probabilistic double machine learning method S. Zhan et al. https://doi.org/10.1016/j.jhydrol.2025.133484
12. Balancing accuracy versus precision: Enhancing the usability of sub-seasonal forecasts E. Dunn-Sigouin et al. https://doi.org/10.1016/j.cliser.2025.100594
13. Multi-Source Remote Sensing for Dynamic Landslide Susceptibility Assessment: From Static Mapping to Spatiotemporal Inference and Updating H. Deng et al. https://doi.org/10.3390/rs18132153