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The discrepancies among the variations in global ice volume, cave stalagmite δ18O and rainfall reconstructed by cosmogenic 10Be tremendously restrain our understanding of the evolution of the East Asian summer monsoon (EASM). Here, we present a 430-ka EASM mean annual precipitation record on the Chinese Loess Plateau obtained using branched glycerol dialkyl glycerol tetraethers based on a deep learning neural network; this rainfall record corresponds well with cave-derived δ18O data from southern China but differs from precipitation reconstructed by 10Be. Both branched tetraether membrane lipids and cave δ18O may be affected by soil moisture and atmospheric temperature when glacial and interglacial conditions alternated and were thus decoupled from atmospheric precipitation; instead, they represent variations in the intensity of the EASM. Furthermore, we demonstrate that the brGDGT-DLNN method can significantly extend the temporal scale record of the EASM and is not restricted by geographic location compared with stalagmite records. In considering Asian monsoon intensity and precipitation during glacial-interglacial transitions in Chinese Loess Plateau, a new study finds that brGDGT-DLNN method can significantly extend the temporal scale record of the EASM and is not restricted by geographic location compared with stalagmite records. [ABSTRACT FROM AUTHOR]