This study proposes a novel framework for predicting evaporative fraction (EF) by explicitly incorporating meteorological memory into machine learning models. By demonstrating that long short-term memory (LSTM) networks substantially outperform conventional approaches across a range of ecosystems, the study reveals that vegetation responses to water availability are fundamentally governed by temporal dependencies that are often neglected in current modeling frameworks. This work provides new process-level understanding of land–atmosphere interactions.