Projecting Actual Evapotranspiration (AET) is critical for the survival of semi-arid Pinus sylvestris var. mongolica forests but remains difficult due to climate uncertainties. We bridged this gap by developing a hybrid framework in Inner Mongolia. We trained a Long Short-Term Memory (LSTM) network using SEBAL-derived AET as a physics-based proxy target. This approach achieved high accuracy in simulating historical dynamics (R2 = 0.926, RMSE = 13.56 mm/month). Crucially, our model relies on high-resolution data from 2021 to learn intra-annual seasonality; consequently, our projections represent responses to mean climatological shifts rather than interannual variability. To assess future risks, we drove this validated model with an ensemble of five bias-corrected CMIP6 General Circulation Models (GCMs) for the 2030–2050 period under SSP2-4.5 and SSP5-8.5 scenarios. The ensemble projections reveal a robust increase in total annual AET, driven by a predicted ‘warmer and wetter’ climate (+2.4°C temperature, +12% precipitation). However, this increase is uneven, showing a significant intensification specifically during the growing season (May–August). This seasonal spike indicates a heightened risk of rapid soil moisture depletion due to soaring atmospheric demand, paradoxically creating water stress despite higher annual rainfall. These findings challenge conventional views and highlight the urgent need for adaptive management focused on seasonal vulnerability.
Su et al. (2026) studied this question.