| Alzubaidi, L., and Coauthors, 2021: Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 53, https://doi.org/10.1186/s40537-021-00444-8. |
| Asfaw, T. G., and J. J. Luo, 2022: Seasonal prediction of summer precipitation over East Africa using NUIST-CFS1.0. Adv. Atmos. Sci., 39, 355−372, https://doi.org/10.1007/s00376-021-1180-1. |
| Baño-Medina, J., R. Manzanas, and J. M. Gutiérrez, 2020: Configuration and intercomparison of deep learning neural models for statistical downscaling. Geoscientific Model Development, 13, 2109−2124, https://doi.org/10.5194/gmd-13-2109-2020. |
| Baño-Medina, J., R. Manzanas, and J. M. Gutiérrez, 2021: On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections. Climate Dyn., 57, 2941−2951, https://doi.org/10.1007/s00382-021-05847-0. |
| Baño-Medina, J., R. Manzanas, E. Cimadevilla, J. Fernández, J. González-Abad, A. S. Cofiño, and J. M. Gutiérrez, 2022: Downscaling multi-model climate projection ensembles with deep learning (DeepESD): Contribution to CORDEX EUR-44. Geoscientific Model Development, 15, 6747−6758, https://doi.org/10.5194/gmd-15-6747-2022. |
| Bedia, J., and Coauthors, 2020: Statistical downscaling with the downscaleR package (v3.1.0): Contribution to the VALUE intercomparison experiment. Geoscientific Model Development, 13, 1711−1735, https://doi.org/10.5194/gmd-13-1711-2020. |
| Bergstra, J., and Y. Bengio, 2012: Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 13, 281−305. |
| Bhend, J., I. Mahlstein, and M. A. Liniger, 2017: Predictive skill of climate indices compared to mean quantities in seasonal forecasts. Quart. J. Roy. Meteor. Soc., 143, 184−194, https://doi.org/10.1002/qj.2908. |
| Brands, S., J. M. Gutiérrez, S. Herrera, and A. S. Cofiño, 2012: On the use of reanalysis data for downscaling. J. Climate, 25, 2517−2526, https://doi.org/10.1175/JCLI-D-11-00251.1. |
| Bruyère, C. L., J. M. Done, G. J. Holland, and S. Fredrick, 2014: Bias corrections of global models for regional climate simulations of high-impact weather. Climate Dyn., 43, 1847−1856, https://doi.org/10.1007/s00382-013-2011-6. |
| Buontempo, C., and C. Hewitt, 2018: EUPORIAS and the development of climate services. Climate Services, 9, 1−4, https://doi.org/10.1016/j.cliser.2017.06.011. |
| Bürger, G., T. Q. Murdock, A. T. Werner, S. R. Sobie, and A. J. Cannon, 2012: Downscaling extremes-an intercomparison of multiple statistical methods for present climate. J. Climate, 25, 4366−4388, https://doi.org/10.1175/JCLI-D-11-00408.1. |
| Camberlin, P., 1997: Rainfall anomalies in the source region of the Nile and their connection with the Indian Summer Monsoon. J. Climate, 10, 1380−1392, https://doi.org/10.1175/1520-0442(1997)010<1380:RAITSR>2.0.CO;2. |
| Cannon, A. J., 2008: Probabilistic multisite precipitation downscaling by an expanded Bernoulli-gamma density network. Journal of Hydrometeorology, 9, 1284−1300, https://doi.org/10.1175/2008JHM960.1. |
| Chen, D., and A. G. Dai, 2019: Precipitation characteristics in the community atmosphere model and their dependence on model physics and resolution. Journal of Advances in Modeling Earth Systems, 11, 2352−2374, https://doi.org/10.1029/2018MS001536. |
| Chen, J., X. J. Zhang, and F. P. Brissette, 2014: Assessing scale effects for statistically downscaling precipitation with GPCC model. International Journal of Climatology, 34, 708−727, https://doi.org/10.1002/joc.3717. |
| Cofiño, A. S., and Coauthors, 2018: The ECOMS user data gateway: Towards seasonal forecast data provision and research reproducibility in the era of Climate Services. Climate Services, 9, 33−43, https://doi.org/10.1016/j.cliser.2017.07.001. |
| Cong, S., and Y. Zhou, 2023: A review of convolutional neural network architectures and their optimizations. Artificial Intelligence Review, 56, 1905−1969, https://doi.org/10.1007/s10462-022-10213-5. |
| Dai, A. G., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 4605−4630, https://doi.org/10.1175/JCLI3884.1. |
| Díez, E., C. Primo, J. A. García-Moya, J. M. Gutiérrez, and B. Orfila, 2005: Statistical and dynamical downscaling of precipitation over Spain from DEMETER seasonal forecasts. Tellus A, 57, 409−423, https://doi.org/10.1111/j.1600-0870.2005.00130.x. |
| Díez, E., B. Orfila, M. D. Frías, J. Fernández, A. S. Cofiño, and J. M. Gutiérrez, 2011: Downscaling ECMWF seasonal precipitation forecasts in Europe using the RCA model. Tellus A, 63, 757−762, https://doi.org/10.1111/j.1600-0870.2011.00523.x. |
| Di Luca, A., R. de Elía, and R. Laprise, 2015: Challenges in the quest for added value of regional climate dynamical downscaling. Current Climate Change Reports, 1, 10−21, https://doi.org/10.1007/s40641-015-0003-9. |
| Diro, G. T., A. M. Tompkins, and X. Bi, 2012: Dynamical downscaling of ECMWF Ensemble seasonal forecasts over East Africa with RegCM3. J. Geophys. Res.: Atmos., 117, D16103, https://doi.org/10.1029/2011JD016997. |
| Doblas-Reyes, F. J., and C. M. Goodess, 2005: Working paper on the need for downscaling of seasonal-to-decadal integrations within the EU-funded ENSEMBLES project. ENSEMBLES Technical Report No. 2, 1−10. |
| Dumoulin, V., and F. Visin, 2018: A guide to convolution arithmetic for deep learning. https://doi.org/10.48550/ARXIV.1603.07285. |
| Funk, C., and Coauthors, 2015: The climate hazards infrared precipitation with stations--A new environmental record for monitoring extremes. Scientific Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66. |
| Giorgi, F., and W. J. Gutowski, 2015: Regional dynamical downscaling and the CORDEX initiative. Annual Review of Environment and Resources, 40, 467−490, https://doi.org/10.1146/annurev-environ-102014-021217. |
| Gutiérrez, J. M., D. San-Martín, S. Brands, R. Manzanas, and S. Herrera, 2013: Reassessing statistical downscaling techniques for their robust application under climate change conditions. J. Climate, 26, 171−188, https://doi.org/10.1175/JCLI-D-11-00687.1. |
| Gutiérrez, J. M., and Coauthors, 2019: An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment. International Journal of Climatology, 39, 3750−3785, https://doi.org/10.1002/joc.5462. |
| Gutmann, E. D., R. M. Rasmussen, C. H. Liu, K. Ikeda, D. J. Gochis, M. P. Clark, J. Dudhia, and G. Thompson, 2012: A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain. J. Climate, 25, 262−281, https://doi.org/10.1175/2011JCLI4109.1. |
| Gutowski, W. J., and Coauthors, 2020: The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information. Bull. Amer. Meteor. Soc., 101, E664−E683, https://doi.org/10.1175/BAMS-D-19-0113.1. |
| Hansen, J. W., S. J. Mason, L. Q. Sun, and A. Tall, 2011: Review of seasonal climate forecasting for agriculture in sub-Saharan Africa. Experimental Agriculture, 47, 205−240, https://doi.org/10.1017/S0014479710000876. |
| Harrison, M., A. Kanga, G. O. Magrin, G. Hugo, I. Tarakidzwa, C. Mullen, and H. Meinke, 2007: Use of seasonal forecasts and climate prediction in operational agriculture. World Meteorological Organization Commission for Agricultural Meteorology, CAgM Rep. No. 102, 87 pp. |
| He, X. G., N. W. Chaney, M. Schleiss, and J. Sheffield, 2016: Spatial downscaling of precipitation using adaptable random forests. Water Resour. Res., 52, 8217−8237, https://doi.org/10.1002/2016WR019034. |
| Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999−2049, https://doi.org/10.1002/qj.3803. |
| Hess, P., and N. Boers, 2022: Deep learning for improving numerical weather prediction of heavy rainfall. Journal of Advances in Modeling Earth Systems, 14, e2021MS002765, https://doi.org/10.1029/2021MS002765. |
| Hewitson, B. C., and R. G. Crane, 1996: Climate downscaling: Techniques and application. Climate Research, 7, 85−95, https://doi.org/10.3354/cr007085. |
| Katz, R. W., and B. G. Brown, 1992: Extreme events in a changing climate: Variability is more important than averages. Climate Change, 21, 289−302, https://doi.org/10.1007/BF00139728. |
| Kipkogei, O., A. M. Mwanthi, J. B. Mwesigwa, Z. K. K. Atheru, M. A. Wanzala, and G. Artan, 2017: Improved seasonal prediction of rainfall over East Africa for application in agriculture: Statistical downscaling of CFSv2 and GFDL-FLOR. J. Appl. Meteorol. Climatol., 56, 3229−3243, https://doi.org/10.1175/JAMC-D-16-0365.1. |
| Korecha, D., and A. G. Barnston, 2007: Predictability of June–September rainfall in Ethiopia. Mon. Wea. Rev., 135, 628−650, https://doi.org/10.1175/MWR3304.1. |
| Lecun, Y., Y. Bengio, and G. Hinton, 2015: Deep learning. Nature, 521, 436−444, https://doi.org/10.1038/nature14539. |
| Legasa, M. N., S. Thao, M. Vrac, and R. Manzanas, 2023: Assessing three perfect prognosis methods for statistical downscaling of climate change precipitation scenarios. Geophys. Res. Lett., 50, e2022GL102525, https://doi.org/10.1029/2022GL102525. |
| Lorenz, E. N., 1969: Atmospheric predictability as revealed by naturally occurring analogues. J. Atmos. Sci., 26, 636−646, https://doi.org/10.1175/1520-0469(1969)26<636:APARBN>2.0.CO;2. |
| Manzanas, R., M. D. Frías, A. S. Cofiño, and J. M. Gutiérrez, 2014: Validation of 40 year multimodel seasonal precipitation forecasts: The role of enso on the global skill. J. Geophys. Res.: Atmos., 119, 1708−1719, https://doi.org/10.1002/2013JD020680. |
| Manzanas, R., A. Lucero, A. Weisheimer, and J. M. Gutiérrez, 2018b: Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts. Climate Dyn., 50, 1161−1176, https://doi.org/10.1007/s00382-017-3668-z. |
| Manzanas, R., S. Brands, D. San-Martín, A. Lucero, C. Limbo, and J. M. Gutiérrez, 2015: Statistical downscaling in the tropics can be sensitive to reanalysis choice: A case study for precipitation in the Philippines. J. Climate, 28, 4171−4184, https://doi.org/10.1175/JCLI-D-14-00331.1. |
| Manzanas, R., J. M. Gutiérrez, J. Fernández, E. van Meijgaard, S. Calmanti, M. E. Magariño, A. S. Cofiño, and S. Herrera, 2018a: Dynamical and statistical downscaling of seasonal temperature forecasts in Europe: Added value for user applications. Climate Services, 9, 44−56, https://doi.org/10.1016/j.cliser.2017.06.004. |
| Manzanas, R., J. M. Gutiérrez, J. Bhend, S. Hemri, F. J. Doblas-Reyes, V. Torralba, E. Penabad, and A. Brookshaw, 2019: Bias adjustment and ensemble recalibration methods for seasonal forecasting: A comprehensive intercomparison using the C3S dataset. Climate Dyn., 53, 1287−1305, https://doi.org/10.1007/s00382-019-04640-4. |
| Maraun, D., 2012: Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys. Res. Lett., 39, L06706, https://doi.org/10.1029/2012GL051210. |
| Maraun, D., and M. Widmann, 2018: Statistical Downscaling and Bias Correction for Climate Research. Cambridge University Press, 347 pp. |
| Maraun, D., M. Widmann, and J. M. Gutiérrez, 2019: Statistical downscaling skill under present climate conditions: A synthesis of the VALUE perfect predictor experiment. International Journal of Climatology, 39, 3692−3703, https://doi.org/10.1002/joc.5877. |
| Maraun, D., and Coauthors, 2010: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314. |
| Maraun, D., and Coauthors, 2015: VALUE: A framework to validate downscaling approaches for climate change studies. Earth’s Future, 3, 1−14, https://doi.org/10.1002/2014EF000259. |
| Meza, F. J., J. W. Hansen, and D. Osgood, 2008: Economic value of seasonal climate forecasts for agriculture: Review of ex-ante assessments and recommendations for future research. J. Appl. Meteorol. Climatol., 47, 1269−1286, https://doi.org/10.1175/2007JAMC1540.1. |
| Mori, P., T. Schwitalla, M. B. Ware, K. Warrach-Sagi, and V. Wulfmeyer, 2021: Downscaling of seasonal ensemble forecasts to the convection-permitting scale over the Horn of Africa using the WRF model. International Journal of Climatology, 41, E1791−E1811, https://doi.org/10.1002/joc.6809. |
| Nelder, J. A., and R. W. M. Wedderburn, 1972: Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135, 370−384, https://doi.org/10.2307/2344614. |
| Nicholson, S. E., 2017: Climate and climatic variability of rainfall over eastern Africa. Rev. Geophys., 55, 590−635, https://doi.org/10.1002/2016RG000544. |
| Nikulin, G., and Coauthors, 2018: Dynamical and statistical downscaling of a global seasonal hindcast in eastern Africa. Climate Services, 9, 72−85, https://doi.org/10.1016/j.cliser.2017.11.003. |
| Ordoñez, L., and Coauthors, 2022: Applying agroclimatic seasonal forecasts to improve rainfed maize agronomic management in Colombia. Climate Services, 28, 100333, https://doi.org/10.1016/j.cliser.2022.100333. |
| Pan, B. X., K. Hsu, A. AghaKouchak, and S. Sorooshian, 2019: Improving precipitation estimation using convolutional neural network. Water Resour. Res., 55, 2301−2321, https://doi.org/10.1029/2018WR024090. |
| Pour, S. H., S. Shahid, and E. S. Chung, 2016: A hybrid model for statistical downscaling of daily rainfall. Procedia Engineering, 154, 1424−1430, https://doi.org/10.1016/j.proeng.2016.07.514. |
| Riddle, E. E., and K. H. Cook, 2008: Abrupt rainfall transitions over the Greater Horn of Africa: Observations and regional model simulations. J. Geophys. Res.: Atmos., 113, D15109, https://doi.org/10.1029/2007JD009202. |
| Riesenhuber, M., and T. Poggio, 1999: Hierarchical models of object recognition in cortex. Nature Neuroscience, 2, 1019−1025, https://doi.org/10.1038/14819. |
| Robertson, A. W., J. H. Qian, M. K. Tippett, V. Moron, and A. Lucero, 2012: Downscaling of seasonal rainfall over the Philippines: Dynamical versus statistical approaches. Mon. Wea. Rev., 140, 1204−1218, https://doi.org/10.1175/MWR-D-11-00177.1. |
| Rockel, B., 2015: The regional downscaling approach: A brief history and recent advances. Current Climate Change Reports, 1, 22−29, https://doi.org/10.1007/s40641-014-0001-3. |
| Ronneberger, O., P. Fischer, and T. Brox, 2015: U-net: Convolutional networks for biomedical image segmentation. Preprints, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, Springer, 234−241, https://doi.org/10.1007/978-3-319-24574-4_28. |
| San-Martín, D., R. Manzanas, S. Brands, S. Herrera, and J. M. Gutiérrez, 2017: Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods. J. Climate, 30, 203−223, https://doi.org/10.1175/JCLI-D-16-0366.1. |
| Sariturk, B., D. Z. Seker, O. Ozturk, and B. Bayram, 2022: Performance evaluation of shallow and deep CNN architectures on building segmentation from high-resolution images. Earth Science Informatics, 15, 1801−1823, https://doi.org/10.1007/s12145-022-00840-5. |
| Seregina, L. S., A. H. Fink, R. van der Linden, N. A. Elagib, and J. G. Pinto, 2019: A new and flexible rainy season definition: Validation for the Greater Horn of Africa and application to rainfall trends. International Journal of Climatology, 39, 989−1012, https://doi.org/10.1002/joc.5856. |
| Seregina, L. S., A. H. Fink, R. van der Linden, C. Funk, and J. G. Pinto, 2021: Using seasonal rainfall clusters to explain the interannual variability of the rain belt over the Greater Horn of Africa. International Journal of Climatology, 41, E1717−E1737, https://doi.org/10.1002/joc.6802. |
| Sha, Y. K., D. J. Gagne II, G. West, and R. Stull, 2020: Deep-learning-based gridded downscaling of surface meteorological variables in complex terrain. Part II: Daily precipitation. J. Appl. Meteorol. Climatol., 59, 2075−2092, https://doi.org/10.1175/JAMC-D-20-0058.1. |
| Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, 2014: Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15, 1929−1958. |
| Sun, L., and Y. F. Lan, 2021: Statistical downscaling of daily temperature and precipitation over China using deep learning neural models: Localization and comparison with other methods. International Journal of Climatology, 41, 1128−1147, https://doi.org/10.1002/joc.6769. |
| Sun, Y., S. Solomon, A. G. Dai, and R. W. Portmann, 2006: How often does it rain. J. Climate, 19, 916−934, https://doi.org/10.1175/JCLI3672.1. |
| Tang, J. P., X. R. Niu, S. Y. Wang, H. X. Gao, X. Y. Wang, and J. Wu, 2016: Statistical downscaling and dynamical downscaling of regional climate in China: Present climate evaluations and future climate projections. J. Geophys. Res.: Atmos., 121, 2110−2129, https://doi.org/10.1002/2015JD023977. |
| Tian, D., C. J. Martinez, W. D. Graham, and S. Hwang, 2014: Statistical downscaling multimodel forecasts for seasonal precipitation and surface temperature over the Southeastern United States. J. Climate, 27, 8384−8411, https://doi.org/10.1175/JCLI-D-13-00481.1. |
| Tripathi, S., V. V. Srinivas, and R. S. Nanjundiah, 2006: Downscaling of precipitation for climate change scenarios: A support vector machine approach. J. Hydrol., 330, 621−640, https://doi.org/10.1016/j.jhydrol.2006.04.030. |
| Tucker, S., R. G. Jones, E. Buonomo, L. Burgin, and F. Gallo, 2018: Dynamical downscaling of GloSea5 over Ethiopia. Climate Services, 9, 57−71, https://doi.org/10.1016/j.cliser.2018.02.001. |
| Vaittinada Ayar, P., M. Vrac, S. Bastin, J. Carreau, M. Déqué, and C. Gallardo, 2016: Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: Present climate evaluations. Climate Dyn., 46, 1301−1329, https://doi.org/10.1007/s00382-015-2647-5. |
| Vandal, T., E. Kodra, and A. R. Ganguly, 2019: Intercomparison of machine learning methods for statistical downscaling: The case of daily and extreme precipitation. Theor. Appl. Climatol., 137, 557−570, https://doi.org/10.1007/s00704-018-2613-3. |
| Vaughan, A., W. Tebbutt, J. S. Hosking, and R. E. Turner, 2022: Convolutional conditional neural processes for local climate downscaling. Geoscientific Model Development, 15, 251−268, https://doi.org/10.5194/gmd-15-251-2022. |
| Viste, E., and A. Sorteberg, 2013a: Moisture transport into the Ethiopian highlands. International Journal of Climatology, 33, 249−263, https://doi.org/10.1002/joc.3409. |
| Viste, E., and A. Sorteberg, 2013b: The effect of moisture transport variability on Ethiopian summer precipitation. International Journal of Climatology, 33, 3106−3123, https://doi.org/10.1002/joc.3566. |
| Wang, F., D. Tian, L. Lowe, L. Kalin, and J. Lehrter, 2021: Deep learning for daily precipitation and temperature downscaling. Water Resour. Res., 57, e2020WR029308, https://doi.org/10.1029/2020wr029308. |
| Weyn, J. A., D. R. Durran, and R. Caruana, 2020: Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere. Journal of Advances in Modeling Earth Systems, 12, e2020MS002109, https://doi.org/10.1029/2020MS002109. |
| Wilby, R. L., S. P. Charles, E. Zorita, B. Timbal, P. Whetton, and L. O. Mearns, 2004: Guidelines for use of climate scenarios developed from statistical downscaling methods. Analysis, 27, 1−27. |
| Wilby, R. L., T. M. L. Wigley, D. Conway, P. D. Jones, B. C. Hewitson, J. Main, and D. S. Wilks, 1998: Statistical downscaling of general circulation model output: A comparison of methods. Water Resour. Res., 34, 2995−3008, https://doi.org/10.1029/98WR02577. |
| Yoon, J. H., L. Ruby Leung, and J. Correia, 2012: Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States. J. Geophys. Res.: Atmos., 117, D21109, https://doi.org/10.1029/2012JD017650. |
| Zeiler, M. D., D. Krishnan, G. W. Taylor, and R. Fergus, 2010: Deconvolutional networks. Preprints, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, IEEE, 2528−2535, https://doi.org/10.1109/CVPR.2010.5539957. |
| Zhang, W. M., M. Brandt, X. Y. Tong, Q. J. Tian, and R. Fensholt, 2018: Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel. Biogeosciences, 15, 319−330, https://doi.org/10.5194/bg-15-319-2018. |