2015, Lange, S., Donges, J.F., Volkholz, J., Kurths, J.

2019, Galmarini, S., Cannon, A.J., Ceglar, A., Christensen, O.B., de Noblet-Ducoudré, N., Dentener, F., Doblas-Reyes, F.J., Dosio, A., Gutierrez, J.M., Iturbide, M., Jury, M., Lange, S., Loukos, H., Maiorano, A., Maraun, D., McGinnis, S., Nikulin, G., Riccio, A., Sanchez, E., Solazzo, E., Toreti, A., Vrac, M., Zampieri, M.

[No abstract available]

2019, Lange, S.

In this paper I present new methods for bias adjustment and statistical downscaling that are tailored to the requirements of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In comparison to their predecessors, the new methods allow for a more robust bias adjustment of extreme values, preserve trends more accurately across quantiles, and facilitate a clearer separation of bias adjustment and statistical downscaling. The new statistical downscaling method is stochastic and better at adjusting spatial variability than the old interpolation method. Improvements in bias adjustment and trend preservation are demonstrated in a cross-validation framework.