Garcia, D., Rolim, J., Cameira, M. D. R., Belaud, G., Dalezios, N. R., Karoutsos, G., Santos, J. A., and Paredes, P.: Assessing seasonal forecast performance to predict crop irrigation requirements to support water management decision-making in the Mediterranean region, Agr. Water Manage., 313, 109467, https://doi.org/10.1016/j.agwat.2025.109467, 2025.
Graham, R. M., Browell, J., Bertram, D., and White, C. J.: The application of sub-seasonal to seasonal (S2S) predictions for hydropower forecasting, arXiv [preprint], https://doi.org/10.48550/arxiv.2108.06269, 2021.
GSI Environmental Inc.: Evaluation of rain gauge and Doppler radar data in the Integrated Northern Tampa Bay Hydrologic Model Domain, Tech. Rep., prepared for Tampa Bay Water, Clearwater, Florida, USA,
https://tampabaywater.sharefile.com/share/view/65beb75f65584df0 (last access: 30 April 2026), 2021a.
GSI Environmental Inc.: Integration of rainfall data from rain gauges and Doppler radar in the Integrated Northern Tampa Bay Hydrologic Model Domain, Tech. Rep., prepared for Tampa Bay Water, Clearwater, Florida, USA,
https://tampabaywater.sharefile.com/share/view/36cb78a35b924cc3 (last access: 30 April 2026), 2021b.
GSI Environmental Inc.: Supplemental report on integration of rainfall data from rain gauges and Doppler radar in the Integrated Northern Tampa Bay Hydrologic Model Domain for 2017 through 2019, Tech. Rep., prepared for Tampa Bay Water, Clearwater, Florida, USA,
https://tampabaywater.sharefile.com/share/view/e5f63e07492d4c5d (last access: 30 April 2026), 2021c.
Hartmann, H. C., Pagano, T. C., Sorooshian, S., and Bales, R.: Confidence Builders: Evaluating Seasonal Climate Forecasts from User Perspectives, B. Am. Meteorol. Soc., 83, 683–698, https://doi.org/10.1175/1520-0477(2002)083<0683:CBESCF>2.3.CO;2, 2002.
Heidke, P.: Berechnung Des Erfolges Und Der Güte Der Windstärkevorhersagen Im Sturmwarnungsdienst, Geogr. Ann., 8, 301–349, https://doi.org/10.1080/20014422.1926.11881138, 1926.
Huang, Z. and Zhao, T.: Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes, WIREs Water, 9, e1580, https://doi.org/10.1002/wat2.1580, 2022.
Hurkmans, R. T. W. L., Van Den Hurk, B., Schmeits, M., Wetterhall, F., and Pechlivanidis, I. G.: Seasonal Streamflow Forecasting for Fresh Water Reservoir Management in the Netherlands: An Assessment of Multiple Prediction Systems, J. Hydrometeorol., 24, 1275–1290, https://doi.org/10.1175/JHM-D-22-0107.1, 2023.
Infanti, J. M. and Kirtman, B. P.: Southeastern U.S. Rainfall Prediction in the North American Multi-Model Ensemble, J. Hydrometeorol., 15, 529–550, https://doi.org/10.1175/JHM-D-13-072.1, 2014.
Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., Van Den Dool, H., Saha, S., Mendez, M. P., Becker, E., Peng, P., Tripp, P., Huang, J., DeWitt, D. G., Tippett, M. K., Barnston, A. G., Li, S., Rosati, A., Schubert, S. D., Rienecker, M., Suarez, M., Li, Z. E., Marshak, J., Lim, Y.-K., Tribbia, J., Pegion, K., Merryfield, W. J., Denis, B., and Wood, E. F.: The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction, B. Am. Meteorol. Soc., 95, 585–601, https://doi.org/10.1175/BAMS-D-12-00050.1, 2014.
Liu, J., Yuan, X., Zeng, J., Jiao, Y., Li, Y., Zhong, L., and Yao, L.: Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning, Hydrol. Earth Syst. Sci., 26, 265–278, https://doi.org/10.5194/hess-26-265-2022, 2022.
Livezey, R. E. and Timofeyeva, M. M.: The First Decade of Long-Lead U.S. Seasonal Forecasts: Insights from a Skill Analysis, B. Am. Meteorol. Soc., 89, 843–854, https://doi.org/10.1175/2008BAMS2488.1, 2008.
MacLeod, D., Quichimbo, E. A., Michaelides, K., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Otenyo, E., Segele, Z., Rigby, J. M., Otieno, G., Hassaballah, K., Tadege, A., and Singer, M. B.: Translating seasonal climate forecasts into water balance forecasts for decision making, PLOS Clim., 2, e0000138, https://doi.org/10.1371/journal.pclm.0000138, 2023.
Mason, S. J., Ferro, C. A. T., and Landman, W. A.: Forecasts of “normal,” Q. J. Roy. Meteor. Soc., 147, 1225–1236, https://doi.org/10.1002/qj.3968, 2021.
Murphy, A. H. and Winkler, R. L.: A General Framework for Forecast Verification, Mon. Weather Rev., 115, 1330–1338, https://doi.org/10.1175/1520-0493(1987)115<1330:AGFFFV>2.0.CO;2, 1987.
Namias, J.: long range weather forecasting – history, current status and outlook1, B. Am. Meteorol. Soc., 49, 438–470, https://doi.org/10.1175/1520-0477-49.5.438, 1968.
Nicholls, N.: Long-range weather forecasting: Value, status, and prospects, Rev. Geophys., 18, 771–788, https://doi.org/10.1029/RG018i004p00771, 1980.
NOAA Climate Prediction Center: CPC verification summary: Tutorial (Heidke Skill Score), National Weather Service, National Centers for Environmental Prediction,
https://www.cpc.ncep.noaa.gov/products/verification/summary/index.php?page=tutorial (last access: 12 October 2025), 2005.
NOAA Climate Prediction Center: Long-lead forecast tool discussion and analysis, National Weather Service, National Oceanic and Atmospheric Administration,
https://www.cpc.ncep.noaa.gov/products/predictions/long_range/tools.html (last access: 12 October 2025), 2021.
NOAA Climate Prediction Center: Prognostic discussion for long-lead seasonal outlooks, National Weather Service, National Centers for Environmental Prediction,
https://www.cpc.ncep.noaa.gov/products/predictions/long_range/fxus05.php (last access: 12 October 2025), 2019.
NOAA Climate Prediction Center: North American Multi-Model Ensemble (NMME), National Oceanic and Atmospheri
c Administration,
https://www.cpc.ncep.noaa.gov/products/NMME/about.html (last access: 16 March 2026), 2025.
O'Lenic, E. A., Unger, D. A., Halpert, M. S., and Pelman, K. S.: Developments in Operational Long-Range Climate Prediction at CPC, Weather Forecast., 23, 496–515, https://doi.org/10.1175/2007WAF2007042.1, 2008.
Peng, P., Kumar, A., Halpert, M. S., and Barnston, A. G.: An Analysis of CPC's Operational 0.5-Month Lead Seasonal Outlooks, Weather Forecast., 27, 898–917, https://doi.org/10.1175/WAF-D-11-00143.1, 2012.
Portele, T. C., Lorenz, C., Dibrani, B., Laux, P., Bliefernicht, J., and Kunstmann, H.: Seasonal forecasts offer economic benefit for hydrological decision making in semi-arid regions, Sci. Rep., 11, 10581, https://doi.org/10.1038/s41598-021-89564-y, 2021.
Srikanthan, R. and McMahon, T. A.: Stochastic generation of annual, monthly and daily climate data: A review, Hydrol. Earth Syst. Sci., 5, 653–670, https://doi.org/10.5194/hess-5-653-2001, 2001.
Slater, L. J., Villarini, G., and Bradley, A. A.: Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA, Clim. Dynam., 53, 7381–7396, https://doi.org/10.1007/s00382-016-3286-1, 2019.
Turco, M., Marcos-Matamoros, R., Castro, X., Canyameras, E., and Llasat, M. C.: Seasonal prediction of climate-driven fire risk for decision-making and operational applications in a Mediterranean region, Sci. Total Environ., 676, 577–583, https://doi.org/10.1016/j.scitotenv.2019.04.296, 2019.
Van Den Dool, H. M. and Toth, Z.: Why Do Forecasts for “Near Normal” Often Fail?, Weather Forecast., 6, 76–85, https://doi.org/10.1175/1520-0434(1991)006<0076:WDFFNO>2.0.CO;2, 1991.
Wang, H.: Evaluation of monthly precipitation forecasting skill of the National Multi-model Ensemble in the summer season, Hydrol. Process., 28, 4472–4486, https://doi.org/10.1002/hyp.9957, 2014.
Wang, H. and Asefa, T.: Impact of different types of ENSO conditions on seasonal precipitation and streamflow in the Southeastern United States, Int. J. Climatol., 38, 1438–1451, https://doi.org/10.1002/joc.5257, 2018.
Wang, H., Asefa, T., Bracciano, D., Adams, A., and Wanakule, N.: Proactive water shortage mitigation integrating system optimization and input uncertainty, J. Hydrol., 571, 711–722, https://doi.org/10.1016/j.jhydrol.2019.01.071, 2019.
Wang, H., Asefa, T., and Duncan, J.: Event-based evaluation of operational ENSO forecasting models in 2002–2020: Implications for seasonal water resources management, J. Hydrol., 636, 131295, https://doi.org/10.1016/j.jhydrol.2024.131295, 2024.
Wilks, D. S.: Statistical methods in the atmospheric sciences: an introduction, Academic Press, San Diego, ISBN 978-0127519654, 1995.
Wilks, D. S. and Wilby, R. L.: The weather generation game: a review of stochastic weather models, Progress in Physical Geography: Earth and Environment, 23, 329–357, https://doi.org/10.1177/030913339902300302, 1999.
Wilks, D. S.: Statistical methods in the atmospheric sciences, 2nd edn., Elsevier, Amsterdam Paris, ISBN 978-0127519661, 2006.
Zhang, H. and Casey, T.: Verification of Categorical Probability Forecasts, Weather Forecast., 15, 80–89, https://doi.org/10.1175/1520-0434(2000)015<0080:VOCPF>2.0.CO;2, 2000.
Zhao, T., Bennett, J. C., Wang, Q. J., Schepen, A., Wood, A. W., Robertson, D. E., and Ramos, M.-H.: How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts?, J. Climate, 30, 3185–3196, https://doi.org/10.1175/JCLI-D-16-0652.1, 2017.
