A data-driven method for identifying climate drivers of agricultural yield failure from daily weather data

Sweet, Lily-belle; Müller, Christoph; Jägermeyr, Jonas; Zscheischler, Jakob

doi:10.5194/gmd-19-6687-2026

Articles | Volume 19, issue 14

https://doi.org/10.5194/gmd-19-6687-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-6687-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 14

Methods for assessment of models

|

23 Jul 2026

Methods for assessment of models |

| 23 Jul 2026

A data-driven method for identifying climate drivers of agricultural yield failure from daily weather data

Lily-belle Sweet, Christoph Müller, Jonas Jägermeyr, and Jakob Zscheischler

Data sets

A data-driven method for identifying climate drivers of agricultural yield failure from daily weather data Lily-belle Sweet https://doi.org/10.5281/zenodo.15725040

ISIMIP3a atmospheric climate input data S. Lange et al. https://doi.org/10.48364/ISIMIP.982724.1

Model code and software

A data-driven method for identifying climate drivers of agricultural yield failure from daily weather data Lily-belle Sweet https://doi.org/10.5281/zenodo.15725040

Short summary

This study presents a method to identify climate drivers of an impact, such as agricultural yield failure, from high-resolution weather data. The approach systematically generates, selects and combines predictors that generalise across different environments. Tested on crop model simulations, the identified drivers are used to create parsimonious models that achieve high predictive performance over long time horizons, offering a more interpretable alternative to black-box models.