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Abstract. Crop growth in land surface models normally requires high-temporal-resolution climate data (3-hourly or 6-hourly), but such high-temporal-resolution climate data are not provided by many climate model simulationsdue to expensive storage, which limits modeling choices if there is aninterest in a particular climate simulation that only saved monthly outputs.The Community Land Surface Model (CLM) has proposed an alternative approachfor utilizing monthly climate outputs as forcing data since version 4.5, andit is called the anomaly forcing CLM. However, such an approach has neverbeen validated for crop yield projections. In our work, we created anomalyforcing datasets for three climate scenarios (1.5 ∘C warming, 2.0 ∘C warming, and RCP4.5) and validated crop yields against thestandard CLM forcing with the same climate scenarios using 3-hourly data. Wefound that the anomaly forcing CLM could not produce crop yields identicalto the standard CLM due to the different submonthly variations, cropyields were underestimated by 5 %–8 % across the three scenarios (1.5, 2.0 ∘C, and RCP4.5) for the global average, and28 %–41 % of cropland showed significantly different yields. However, theanomaly forcing CLM effectively captured the relative changes betweenscenarios and over time, as well as regional crop yield variations. Werecommend that such an approach be used for qualitative analysis of cropyields when only monthly outputs are available. Our approach can be adoptedby other land surface models to expand their capabilities for utilizingmonthly climate data.

Using the anomaly forcing Community Land Model (CLM 4.5) for crop yield projections