Benestad, R. E. and Haugen, J. E.: On Complex Extremes: Flood hazards and combined high spring-time precipitation and temperature in Norway, Climatic Change, 85, 381–406, 2007. a
Benestad, R. E., Chen, D., Mezghani, A., Fan, L., and Parding, K.: On using principal components to represent stations in empirical-statistical downscaling, Tellus A, 67,
https://doi.org/10.3402/tellusa.v67.28326, 2015a.
a
Benestad, R. E., Parding, K. M., Isaksen, K., and Mezghani, A.: Climate change and projections for the Barents region: what is expected to change and what will stay the same?, Environ. Res. Lett., 11, 054017,
https://doi.org/10.1088/1748-9326/11/5/054017, 2016.
a
Benestad, R. E., Parding, K. M., Erlandsen, H. B., and Mezghani, A.: A simple equation to study changes in rainfall statistics, Environ. Res. Lett., 14, 084017,
https://doi.org/10.1088/1748-9326/ab2bb2, 2019.
a,
b
Benestad, R. E., Parding, K. M., and Dobler, A.: Downscaling the probability of heavy rainfall over the Nordic countries, Hydrol. Earth Syst. Sci., 29, 45–65,
https://doi.org/10.5194/hess-29-45-2025, 2025.
a,
b
Bergström, S.: Development and application of a conceptual runoff model for Scandinavian catchments, RHO 7, SMHI, Norrköping, Sweden,
https://www.diva-portal.org/smash/get/diva2:1456191/FULLTEXT01.pdf (last access: 11 November 2026), 1976. a
Deser, C., Knutti, R., Solomon, S., and Phillips, A. S.: Communication of the role of natural variability in future North American climate, Nat. Clim. Change, 2, 775–779,
https://doi.org/10.1038/nclimate1562, 2012.
a,
b
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio, P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E., Kay, J. E., Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S., Randerson, J., Screen, J. A., Simpson, I. R., and Ting, M.: Insights from Earth system model initial-condition large ensembles and future prospects, Nat. Clim. Change, 10, 277–286,
https://doi.org/10.1038/s41558-020-0731-2, 2020.
a,
b
Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G., and Vose, R. S.: Comprehensive Automated Quality Assurance of Daily Surface Observations, J. Appl. Meteorol. Clim., 49, 1615–1633,
https://doi.org/10.1175/2010JAMC2375.1, 2010.
a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958,
https://doi.org/10.5194/gmd-9-1937-2016, 2016.
a
Fowler, H. J., Blenkinsop, S., and Tebaldi, C.: Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling: ADVANCES IN DOWNSCALING TECHNIQUES FOR HYDROLOGICAL MODELLING, Int. J. Climatol., 27, 1547–1578,
https://doi.org/10.1002/joc.1556, 2007.
a
Fowler, H. J., Mearns, L. O., and Wilby, R. L.: Downscaling Future Climate Projections: Compounding Uncertainty But Adding Value?, in: Uncertainty in Climate Change Research, edited by: Mearns, L. O., Forest, C. E., Fowler, H. J., Lempert, R., and Wilby, R. L., pp. 185–197, Springer Nature Switzerland, Cham,
https://doi.org/10.1007/978-3-031-85542-9_18, 2025.
a
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 150066,
https://doi.org/10.1038/sdata.2015.66, 2015.
a
Gassman, P. W., Sadeghi, A. M., and Srinivasan, R.: Applications of the SWAT Model Special Section: Overview and Insights, J. Environ. Qual., 43, 1–8,
https://doi.org/10.2134/jeq2013.11.0466, 2014.
a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Tech. rep., Cambridge University Press,
https://doi.org/10.1017/9781009157896, 2021.
a
IPCC: Climate Change 2022 – Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 1st Edn.,
https://doi.org/10.1017/9781009325844, 2023.
a
Klein Tank, A. J. B. W., Konnen, G. P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., Heino, R., Bessemoulin, P., Müller-Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., van Engelen, A. F. V., Førland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., López, J. A., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexander, L. V., and Petrovic, P.: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment, Int. J. Climatol., 22, 1441–1453,
https://doi.org/10.1002/joc.773, 2002.
a,
b
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288,
https://doi.org/10.1016/S0022-1694(97)00041-3, 1997.
a
Meehl, G. A., Moss, R., Taylor, K. E., Eyring, V., Stouffer, R. J., Bony, S., and Stevens, B.: Climate Model Intercomparisons: Preparing for the Next Phase, Eos T. Am. Geophys. Un., 95, 77–78,
https://doi.org/10.1002/2014EO090001, 2014.
a
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An Overview of the Global Historical Climatology Network-Daily Database, J. Atmos. Ocean. Tech., 29, 897–910,
https://doi.org/10.1175/JTECH-D-11-00103.1, 2012.
a
Mezghani, A. and Hingray, B.: A combined downscaling-disaggregation weather generator for stochastic generation of multisite hourly weather variables over complex terrain: Development and multi-scale validation for the Upper Rhone River basin, J. Hydrol., 377, 245–260,
https://doi.org/10.1016/j.jhydrol.2009.08.033, 2009.
a
Mtongori, H., Stordal, F., Benestad, R., Mourice, S., Pereira-Flores, M., and Justino, F.: Impacts of climate and farming management on maize yield in southernTanzania, Afr. Crop Sci. J., 23, 399,
https://doi.org/10.4314/acsj.v23i4.9, 2015.
a
Schuler, T. V., Benestad, R. E., Isaksen, K., Kierulf, H. P., Kohler, J., Moholdt, G., and Schmidt, L. S.: Svalbard’s 2024 record summer: An early view of Arctic glacier meltdown?, P. Natl. Acad. Sci. USA, 122, e2503806122,
https://doi.org/10.1073/pnas.2503806122, 2025.
a
Semenov, M.: Development of high-resolution UKCIP02-based climate change scenarios in the UK, Agr. Forest Meteorol., 144, 127138, 2007. a
Semenov, M. and Barrow, E. M.: Use of a stochastic weather generator in the development of climate change scenarios, Climate Change, 35, 397–414, 1997. a
Semenov, M. and Brooks, R. J.: Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain, Clim. Res., 11, 137–148, 1999. a
Takayabu, I., Kanamaru, H., Dairaku, K., Benestad, R., Storch, H. V., and Christensen, J. H.: Reconsidering the quality and utility of downscaling, J. Meteor. Soc. Jpn, 94A, 31–45,
https://doi.org/10.2151/jmsj.2015-042, 2015.
a,
b
Van De Velde, J., Demuzere, M., De Baets, B., and Verhoest, N.: Future multivariate weather generation by combining Bartlett-Lewis and vine copula models, Hydrolog. Sci. J., 68, 1–15,
https://doi.org/10.1080/02626667.2022.2144322, 2023.
a
Wilks, D.: Adapting stochastic weather generation algorithms for climate change studies, Climatic Change, 22, 67–84, 1992. a
