CROMES v1.0: a flexible CROp Model Emulator Suite for climate impact assessment

Folberth, C. ORCID: https://orcid.org/0000-0002-6738-5238, Baklanov, A. ORCID: https://orcid.org/0000-0003-1599-3618, Khabarov, N. ORCID: https://orcid.org/0000-0001-5372-4668, Oberleitner, T. ORCID: https://orcid.org/0000-0002-1684-5150, Balkovič, J. ORCID: https://orcid.org/0000-0003-2955-4931, & Skalský, R. ORCID: https://orcid.org/0000-0002-0983-6897 (2025). CROMES v1.0: a flexible CROp Model Emulator Suite for climate impact assessment. Geoscientific Model Development 18 (17) 5759-5779. 10.5194/gmd-18-5759-2025.

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Abstract

Global gridded crop models (GGCMs) are simulation tools designed for global, spatially explicit estimation of crop productivity and associated externalities. Key areas for their application are climate impact and adaptation studies. As GGCMs are typically computationally costly and require comprehensive data pre- and post-processing, GGCM emulators are gaining increasing popularity. Earlier emulators have typically been published pre-trained on synthetic weather and management combinations. Here, we present a novel computational pipeline CROp Model Emulator Suite (CROMES) v1.0 that serves for flexibly training GGCM emulators on data commonly available from GGCM simulations. Essentially, CROMES consists of modules to (1) process climate data from daily resolution netCDF files to (sub-)growing season aggregates as climate features, (2) combine various feature types (climate, soil, crop management), (3) train emulators using machine-learning algorithms, and (4) produce predictions. Exemplary, we apply CROMES to train emulators on simulations for rainfed maize from the GGCM EPIC-IIASA and climate projections from a single GCM to subsequently test their skill in predicting crop yields for unseen climate projections from other GCMs. Depending on the training and target data, the regression statistics between GGCM simulations and predictions across all points in time and space are in the ranges R2 = 0.97 to 0.98, slope = 0.99 to 1.01, and intercept = −0.06 to +0.06. The RMSE ranges between 0.49 and 0.65 t ha−1. Spatially, patterns are evident with lowest performance in (semi-)arid regions where aggregation of weather data may result in higher information loss while permanent crop growth limitations may hamper evaluation statistics as well. The gain in computational speed for predictions is at more than an order of magnitude with time required to produce target features and subsequent predictions at about 30min on common hardware. We expect CROMES to be of utility in covering more comprehensively uncertainty in climate impact projections, evaluations of adaptation options, and spatio-temporal assessments of crop productivity.

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