Forest fire modeling often requires estimates of fuel moisture status. Among the various fuel types used for fire modeling studies, the 10-hour fuel moisture content (10-hr FMC) is a promising predictor since it can be automatically measured in real time at study sites, yielding more information for fire models. Here, the performance of 10-hr FMC models based on different approaches, including regression (MREG),machine learning algorithms (MML) with random forest and support vector machine, and a process-based model (MFSMM), were compared. In addition, whole-year models of each type were compared with the irrespective seasonal models to explore whether the development of separate seasonal models yielded better estimates. Meteorological conditions and 10-hr FMC were measured each minute for 18 months in and near a forest site and used for constructing and examining the 10-hr FMC models. In the assessments, MML showed the best performance (R2=0.77~0.82 and root mean squared error [RMSE]=2.05%~2.84%). The introduction of the correction coefficient into MREG improved its estimates (R2 improved from 0.56~0.58 to 0.68~0.70 and RMSE improved from 3.13%~3.85% to 2.64%~3.27%) by reducing the errors associated with high 10-hr FMC values. MFSMM showed the worst performance (R2=0.41~0.43 and RMSE=3.70%~4.39%), which could be attributed to the lack of radiation input from the study site and the more frequent rain events in the study sites. Whole-year models and seasonal models showed almost equal performance because 10-hr FMC varied in response to atmospheric moisture conditions rather specific seasonal patterns. The adoption of a hybrid modeling approach that blends machine learning and process-based approaches may yield better predictability and interpretability. This study provides additional evidence of the lagged response of 10-hr FMC after rainfall, and suggests a new way of accounting for this response in a regression model. Our approach using comparisons among models can be utilized for other fire modeling studies, including those assessing fire danger ratings.