Land evaporation (LET) is an important variable in climate change, water cycle and water resources management. Mastering the projected changes in LET is significant for crop water requirements and the energy cycle. The global climate model (GCM) is a vital tool for future climate change research. However, the GCMs have low spatial resolution and relatively high errors. We use a deep learning (DL)-based model to deal with this problem. The DL approach can downscale the model data and merge simultaneously. We applied the DL approach to a suit of models from the Coupled Model Intercomparison Project 6th edition (CMIP6) LET data. From the result of all the evaluation metrics, the DL merged data greatly improved in both spatial and time dimensions. The mean RMSE is 5.85 mm and the correlation is 0.95 between the DL merged data and reference data (historical reliable evaporation data). The future LET evidently increases in four scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), and the upward intensity rises from the low to high emission scenarios. The highest increasing regions are in the Tibet Plateau and the south of China and the trend is larger than 10 mm/decade in the high scenarios. From the seasonal point of view, the increasing trend in spring and summer is far larger than for autumn and winter. The Tibet Plateau and the northeast of China have the largest upward trend in the spring of SSP5–8.5, higher than 1.6 mm/decade.
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