Biesiada, J. and Duch, W.: Feature Selection for High-Dimensional Data – A Pearson Redundancy Based Filter, in: Computer Recognition Systems 2, edited by: Kurzynski, M., Puchala, E., Wozniak, M., and Zolnierek, A., Springer, Berlin, Heidelberg, 242–249, 2007. a
Ceglar, A., Zampieri, M., Toreti, A., and Dentener, F.: Observed Northward Migration of Agro-Climate Zones in Europe Will Further Accelerate Under Climate Change, Earths Future, 7, 1088–1101,
https://doi.org/10.1029/2019EF001178, 2019.
a
Chollet, F., et al.: Keras,
available at:
https://keras.io (last access: 29 November 2021), 2015.
a,
b
Clevert, D. A., Unterthiner, T., and Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs), arXiv [preprint],
arXiv:1511.07289, 23 November 2015.
a,
b
ClimEx project: CRCM5-LE, available at:
https://climex-data.srv.lrz.de/Public/ (last access: 30 November 2021), 2020. a
Dawson, A.: eofs: A Library for EOF Analysis of Meteorological, Oceanographic, and Climate Data, Journal of Open Research Software, 4, e14,
https://doi.org/10.5334/jors.122, 2016.
a,
b
Deo, R. C., Kisi, O., and Singh, V. P.: Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model, Atmos. Res., 184, 149–175,
https://doi.org/10.1016/j.atmosres.2016.10.004, 2017.
a
Enfield, D. B., Mestas-Nuñez, A. M., and Trimble, P. J.: The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental U.S., Geophys. Res. Lett., 28, 2077–2080,
https://doi.org/10.1029/2000GL012745, 2001.
a
Environment and Climate Change Canada: The Canadian Earth System Model Large Ensembles, available at:
https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c (last access: 29 November 2021), 2020. a
Federal Ministry of Food and Agriculture: Trockenheit und Dürre
2018 – Überblick über Maßnahmen, available at:
https://www.bmel.de/DE/Landwirtschaft/Nachhaltige-Landnutzung/Klimawandel/_Texte/Extremwetterlagen-Zustaendigkeiten.html (last access: 25 July 2019), 2018. a
Folland, C. K., Knight, J., Linderholm, H. W., Fereday, D., Ineson, S., and Hurrell, J. W.: The Summer North Atlantic Oscillation: Past, Present, and Future, J. Climate, 22, 1082–1103,
https://doi.org/10.1175/2008JCLI2459.1, 2009.
a,
b
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, The MIT Press, Cambridge, MA, USA, 2016.
a,
b
Guyon, I. and Elisseeff, A.: An Introduction to Variable and Feature
Selection, J. Mach. Learn. Res., 3, 1157–1182, 2003.
a,
b
Hao, Z., Singh, V. P., and Xia, Y.: Seasonal Drought Prediction: Advances, Challenges, and Future Prospects, Rev. Geophys., 56, 108–141,
https://doi.org/10.1002/2016RG000549, 2018.
a,
b
Hurrell, J. W., Kushnir, Y., Ottersen, G., and Visbeck, M. (Eds.): The North
Atlantic Oscillation: Climatic Significance and Environmental Impact, American
Geophysical Union, Washington, D.C., 2003. a
IPCC: Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to
the Fifth Assessment Report of the IPCC, Assessment report (Intergovernmental Panel on Climate Change), Working Group, Cambridge University Press, Cambridge, England, 2013. a
Kirchmeier-Young, M., Zwiers, F., and Gillett, N.: Attribution of Extreme Events in Arctic Sea Ice Extent, J. Climate, 30, 553–571,
https://doi.org/10.1175/JCLI-D-16-0412.1, 2016.
a
Kushner, P. J., Mudryk, L. R., Merryfield, W., Ambadan, J. T., Berg, A., Bichet, A., Brown, R., Derksen, C., Déry, S. J., Dirkson, A., Flato, G., Fletcher, C. G., Fyfe, J. C., Gillett, N., Haas, C., Howell, S., Laliberté, F., McCusker, K., Sigmond, M., Sospedra-Alfonso, R., Tandon, N. F., Thackeray, C., Tremblay, B., and Zwiers, F. W.: Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system, The Cryosphere, 12, 1137–1156,
https://doi.org/10.5194/tc-12-1137-2018, 2018.
a
Leduc, M., Mailhot, A., Frigon, A., Martel, J.-L., Ludwig, R., Brietzke, G. B., Giguère, M., Brissette, F., Turcotte, R., Braun, M., and Scinocca, J.: The ClimEx Project: A 50-Member Ensemble of Climate Change Projections at 12-km Resolution over Europe and Northeastern North America with the Canadian Regional Climate Model (CRCM5), J. Appl. Meteorol. Clim., 58, 663–693,
https://doi.org/10.1175/JAMC-D-18-0021.1, 2019.
a
Lim, Y.-K.: The East Atlantic/West Russia (EA/WR) teleconnection in the North
Atlantic: climate impact and relation to Rossby wave propagation,
Clim. Dynam., 44, 3211–3222,
https://doi.org/10.1007/s00382-014-2381-4, 2015.
a
Lundberg, S. and Lee, S.: A unified approach to interpreting model
predictions, CoRR, arXiv [preprint],
arXiv:1705.07874, 25 November 2017.
a
Maas, A. L.: Rectifier Nonlinearities Improve Neural Network Acoustic Models,
in: Proceedings of the 30th International Conference on Machine Learning, 16–21 Juni 2013,
Atlanta, Georgia, USA, 2013.
a,
b
Martynov, A., Laprise, R., Sushama, L., Winger, K., Šeparović, L., and Dugas, B.: Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation, Clim. Dynam., 41, 2973–3005,
https://doi.org/10.1007/s00382-013-1778-9, 2013.
a
McGovern, A., Elmore, K. L., Gagne, D. J., Haupt, S. E., Karstens, C. D., Lagerquist, R., Smith, T., and Williams, J. K.: Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather, B. Am. Meteorol. Soc., 98, 2073–2090,
https://doi.org/10.1175/BAMS-D-16-0123.1, 2017.
a,
b
McKee, T., Doesken, N., and Kleist, J.: The relationship of drought frequency
and duration to time scales, in: Proceedings of the 8th Conference on Applied
Climatology, American Meteorological Society Boston, 17–22 January 1993, Anaheim, CA, USA,
1993. a
Morid, S., Smakhtin, V., and Bagherzadeh, K.: Drought forecasting using artificial neural networks and time series of drought indices, Int. J. Climatol., 27, 2103–2111,
https://doi.org/10.1002/joc.1498, 2007.
a,
b
Russell, S. and Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn., Prentice Hall Press, Upper Saddle River, NJ, USA, 2009.
a,
b,
c,
d,
e,
f,
g,
h
Santos, J. F., Portela, M. M., and Pulido-Calvo, I.: Spring drought prediction based on winter NAO and global SST in Portugal, Hydrol. Process., 28, 1009–1024,
https://doi.org/10.1002/hyp.9641, 2014.
a,
b,
c
Sasaki, Y.: The truth about of the F-measure, available at:
https://www.cs.odu.edu/~mukka/cs795sum09dm/Lecturenotes/Day3/F-measure-YS-26Oct07.pdf (last access: 30 November 2021), 2007. a
Sheffield, J. and Wood, E. F.: Drought: past problems and future scenarios, Earthscan, London, Washington, DC, 2011.
a,
b,
c
Sheffield, J., Andreadis, K. M., Wood, E. F., and Lettenmaier, D. P.: Global and Continental Drought in the Second Half of the Twentieth Century: Severity–Area–Duration Analysis and Temporal Variability of Large-Scale Events, J. Climate, 22, 1962–1981,
https://doi.org/10.1175/2008JCLI2722.1, 2009.
a,
b
Spinoni, J., Naumann, G., Vogt, J., and Barbosa, P.: Meteorological Drought in Europe: Events and Impacts: Past Trends and Future Projections, Publications Office of the European Union, Luxembourg, 2016. a
Spinoni, J., Naumann, G., and Vogt, J. V.: Pan-European seasonal trends and recent changes of drought frequency and severity, Global Planet. Change, 148, 113–130,
https://doi.org/10.1016/j.gloplacha.2016.11.013, 2017.
a
von Trentini, F., Aalbers, E. E., Fischer, E. M., and Ludwig, R.: Comparing interannual variability in three regional single-model initial-condition large ensembles (SMILEs) over Europe, Earth Syst. Dynam., 11, 1013–1031,
https://doi.org/10.5194/esd-11-1013-2020, 2020.
a
Yoon, J.-H., Mo, K., and Wood, E. F.: Dynamic-model-based seasonal prediction of meteorological drought over the contiguous United States, J. Hydrometeorol., 13, 463–482, 2012.
a,
b
Zargar, A., Sadiq, R., Naser, B., and Khan, F. I.: A review of drought indices, Environ. Rev., 19, 333–349,
https://doi.org/10.1139/a11-013, 2011.
a
