Machine learning (ML) models are gaining popularity in biogeosciences. They are applied as gap-filling methods and used to upscale carbon fluxes to larger areas. Here we use explainable artificial intelligence (XAI) methods to elucidate the performance of machine learning models for carbon dioxide fluxes in boreal forests. We show that statistically equal models treat input variables differently. XAI methods can help scientists make informed decisions when applying ML models in their research.