Transfer Learning for Transformer‐Based Drought Forecasting Across Diverse Precipitation Products in India

Abstract Effective drought management is critical for agriculture‐based economies like India. This study examines whether a transformer‐based architecture can function as a robust pre‐trained backbone for operational drought forecasting across India. We utilized a pre‐trained transformer model, which was trained on the Indian Monsoon Data Assimilation and Analysis (IMDAA) precipitation data set (source domain) from 1979 to 2014. Transfer learning capabilities were evaluated by applying the pre‐trained model without target‐domain adaptation to 11 precipitation products (target data set) differing in source and spatial resolution. The target data sets were compared with the source domain to examine how well they reproduce regional climatic patterns. Transfer learning performance was evaluated over two distinct periods: the overall study period (2001–2019), partially overlapping with training, and the unseen testing period (2017–2019). Spatial evaluation is performed across six climatic zones delineated based on the Köppen‐Geiger classification. We utilized the 3‐month Standardized Precipitation Evapotranspiration Index (SPEI‐3) to quantify meteorological drought at