  Climate change is expected to increase the likelihood of drought events, with
severe implications for food security. Unlike other natural disasters, droughts
have a slow onset and depend on various external factors, making drought
detection in climate data difficult. In contrast to existing works that rely on
simple relative drought indices as ground-truth data, we build upon soil
moisture index (SMI) obtained from a hydrological model. This index is directly
related to insufficiently available water to vegetation. Given ERA5-Land
climate input data of six months with land use information from MODIS satellite
observation, we compare different models with and without sequential inductive
bias in classifying droughts based on SMI. We use PR-AUC as the evaluation
measure to account for the class imbalance and obtain promising results despite
a challenging time-based split. We further show in an ablation study that the
models retain their predictive capabilities given input data of coarser
resolutions, as frequently encountered in climate models.
