Journal articles

Preprints

• M. Schillinger, M. Samarin, X. Shen, R. Knutti, N. Meinshausen: EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules. 2025. Preprint
• J. Wessel, M. Schillinger, F. Kwasniok, S. Allen: Enforcing tail calibration when training probabilistic forecast models. 2025. Preprint

Published

• I. de Vries, M. Schillinger, E. Fischer, S. Sippel, R. Knutti: Precipitation disaster hotspots depend on historical climate variability. nature communications. 2025. Full paper
• M. Schillinger, B. Ellerhoff, R. Scheichl, K. Rehfeld: Separating internal and externally-forced contributions to global temperature variability using a Bayesian stochastic energy balance framework. Chaos. 2022. Full paper

Conference Contributions

• M. Schillinger, X. Shen, M. Samarin, R. Knutti, N. Meinshausen: Multivariate Generative Downscaling of Climate Simulation Data with Proper Scoring Rules. EXCLAIM Syposium Zurich. 2025. Abstract
• M. Schillinger, X. Shen, M. Samarin, R. Knutti, N. Meinshausen: Multivariate generative downscaling from GCM to RCM data using the energy score. Climate Informatics. 2025.
• M. Schillinger, X. Shen, M. Samarin, N. Meinshausen: Generative Modelling for Multivariate Downscaling via Proper Scoring Rules. IMSC Toulouse. 2024. Slides
• M. Schillinger, X. Shen, M. Samarin, N. Meinshausen: Machine Learning for Multivariate Downscaling: A Generative Model Inspired by Forecast Evaluation. EGU General Assembly. 2024. Abstract
• M. Schillinger, X. Shen, M. Samarin, N. Meinshausen: Machine Learning for High-Resolution Climate Projections: Generative Models Meet Proper Scoring Rules. MathSEE Symposium Karlsruhe. 2023.
• M. Schillinger, B. Ellerhoff, K. Rehfeld, R. Scheichl: Emulating internal and external components of global temperature variability with a stochastic energy balance model and Bayesian approach. EGU General Assembly. 2023. Abstract
• M. Schillinger, B. Ellerhoff, K. Rehfeld, R. Scheichl: Bayesian Inference of Climate Parameters Using Multibox EBMs. EGU General Assembly. 2022. Abstract
• M. Schillinger, B. Ellerhoff, K. Rehfeld, R. Scheichl: Bayesian parameter estimation for EBMs: What can we learn about climate variability? DPG Meeting of the Matter and Cosmos Section (SMuK). 2021.

Software

• M. Schillinger and B. Ellerhoff. ClimBayes, Bayesian inference of climate parameters using multi-box energy balance models (EBMs). 2022. Link to Github