Abstract Process‐based soil carbon (C) models are increasingly used to project regional and global C cycle responses to climate change. However, the development and evaluation of these models has largely focused on temperate regions of North America and Europe. This geographic bias raises a critical question: Do these models capture generalizable mechanisms that can be applied to underrepresented pedological regions or encode processes specific to their developmental context? We evaluated three process‐based models—Century, Millennial, and MIMICS—across 777 topsoil samples spanning the climate and pedological diversity of sub‐Saharan Africa. Despite their differences in mechanistic detail, all three models performed similarly (adjusted R 2 = 0.09–0.18) in predicting soil organic carbon (SOC) stocks. Using random forest algorithms trained on observed and modeled SOC data, we identified divergences between the drivers of SOC. All three models overemphasized net primary productivity as a SOC driver and misrepresented the role of organo‐mineral interactions. Bias analyses revealed that the three process‐based models inadequately capture exchangeable calcium, which is increasingly recognized as an important control on SOC. Notably, increased mechanistic complexity did not improve transferability. These results have significant implications for regional C budgets and global climate projections. They underscore the importance of incorporating region‐specific biogeochemistry into future soil C models in (sub‐)tropical regions to enhance the precision of climate projections.
Fromm et al. (2026) studied this question.