Publications
Download full list of publications (PDF)
Papers
Peer-reviewed journal articles
Gruber, C.*, Funk, H.*, Mittermeier, M., Küchenhoff, H., & Kauermann, G. (2026). Capturing aleatoric uncertainty in climate models. Environmetrics, 37(5). doi:10.1002/env.70108
Paßlack, N., Veit, H., Funk, H., Zentek, J., & Schuchardt, S. (2026). Impact of a dietary fish oil supplementation on the plasma lipidome of healthy adult cats. Metabolites, 16(6), 427. doi:10.3390/metabo16060427
Lonardi, J., Halimeh, S., von Mackensen, S., Kleinlein, L., Fleischer, J., Funk, H., Hölz, J., Holzapfel, J., Juranek, S., Lieftüchter, V., Bidlingmaier, C., & Olivieri, M. (2026). HAEMFIX: Impact of switching from SHL-FIX to EHL-FIX in patients with haemophilia B. Haemophilia, 32(1), 55–62. doi:10.1111/hae.70157
Funk, H., Ludwig, R., Küchenhoff, H., & Nagler, T. (2025). Towards more realistic climate model outputs: A multivariate bias correction based on zero-inflated vine copulas. Journal of the Royal Statistical Society: Series C (Applied Statistics). doi:10.1093/jrsssc/qlaf044
Fischer, S., Zobolas, J., Sonabend, R., Becker, M., Lang, M., Binder, M., Schneider, L., Burk, L., Schratz, P., Jaeger, B. C., Lauer, S. A., Kapsner, L. A., Mücke, M., Wang, Z., Pulatov, D., Ganz, K., Funk, H., Harutyunyan, L., Camilleri, P., Kopper, P., Bender, A., Zhou, B., German, N., Koers, L., Nazarova, A., & Bischl, B. (2025). mlr3extralearners: Expanding the mlr3 ecosystem with community-driven learner integration. Journal of Open Source Software, 10(115), 8331. doi:10.21105/joss.08331
Preprints
Funk, H., Ludwig, R., Küchenhoff, H., & Nagler, T. (2025). Modelling climate variables at high temporal resolution. ESS Open Archive, 40, essoar-173627220.
Conferences
Conference papers
Scholbeck, C. A.*, Funk, H.*, & Casalicchio, G. (2023). Algorithm-agnostic feature attributions for clustering. In L. Longo (Ed.), Explainable Artificial Intelligence (xAI 2023), CCIS 1901, pp. 217–240. Springer, Cham. doi:10.1007/978-3-031-44064-9_13
Funk, H., Becker, C., Hofheinz, A., Xi, G., Zhang, Y., Pfisterer, F., Weigert, M., & Mittermeier, M. (2021). Towards an automated classification of Hess-Brezowsky’s atmospheric circulation patterns “Tief Mitteleuropa” and “Trog Mitteleuropa” using deep learning methods. In Environmental Informatics: Adjunct Proceedings of the 35th EnviroInfo Conference. Shaker Verlag. doi:10.2370/9783844083293
Presentations & posters
Funk, H., Gruber, C., Kauermann, G., Küchenhoff, H., Ludwig, R., & Mittermeier, M. (2026, May 3–8). Uncertainty-aware AI forecasting of European droughts: The role of internal climate variability. EGU General Assembly 2026, Vienna, Austria & Online. doi:10.5194/egusphere-egu26-10474
Funk, H., Boehnisch, A., Ludwig, R., Küchenhoff, H., & Nagler, T. (2024, December). Introducing a novel, multivariate correction for zero-inflated variables for climate impact assessment at a high temporal resolution. AGU Fall Meeting Abstracts, Vol. 24, A54A-05.
Funk, H., Küchenhoff, H., Ludwig, R., & Nagler, T. (2024, July 23). Bias correction: Adjustment of climate model outputs to historical data. Workshop on Dependence Models, Vines, and Their Applications, Garching, Germany.
Sasse, A., Funk, H., Böhnisch, A., & Ludwig, R. (2024). Droughts in Central Europe: Assessing their characteristics and future dynamics in a hydroclimatic single-model large ensemble. AGU Fall Meeting Abstracts, H41I-0652.
Funk, H., Küchenhoff, H., Ludwig, R., & Nagler, T. (2023, September 25–29). Evaluating the structure of bias-corrected climate variables. International Conference on Regional Climate: CORDEX 2023 (ICRC-CORDEX 2023), Trieste, Italy.
Meier, T., Paschan, N., Böhnisch, A., Funk, H., Sasse, A., & Küchenhoff, H. (2023, July 16–21). Analysis of climatological drivers of low-flow events in hydrological Bavaria using large-ensemble climate projections. In Proceedings of the 37th International Workshop on Statistical Modelling (IWSM), Dortmund, Germany.
Code
VBC: vine copula based bias correction for climate models (R package, version 1.0.0). Corrects the multivariate distribution of heavy-tailed, zero-inflated and continuous climate variables. Accompanies the JRSS-C paper above.
FACT: Feature Attributions for Clustering. Transfers algorithm-agnostic interpretability methods to clustering tasks. Accompanies the xAI 2023 paper above. (Formerly on CRAN; source now maintained on GitHub.)
FuzzyDBScan: fuzzy clustering with DBScan. (Formerly on CRAN; source now maintained on GitHub.)
Seminar books
Funk, H., & Küchenhoff, H. (2026). Climate and statistics III. GitHub Pages. henrifnk.github.io/Seminar_ClimateNStatistics26
Funk, H., Sasse, A., Küchenhoff, H., & Ludwig, R. (2025). Climate and statistics II. GitHub Pages. henrifnk.github.io/Seminar_ClimateNStatistics2425
Küchenhoff, H., & Funk, H. (2024). Climate and statistics. GitHub Pages. henrifnk.github.io/Seminar_ClimateNStatistics
* shared first authorship.
Find the most up-to-date list on ResearchGate or Google Scholar. A few of the contributions here fall outside climate science: they come out of my work advising researchers through StaBLab, LMU’s statistical consulting unit.