Interpretable Temperature‐Based Deep Learning for Evapotranspiration: <scp>SHAP</scp> ‐Based Feature Analysis in <scp>CNN</scp> ‐ <scp>GPU</scp>

ABSTRACT Reference evapotranspiration ( ET o ) is a critical parameter for assessing crop water requirement and formulating irrigation scheduling and water management practices under climate change conditions and water shortage. Classical approaches e.g., the FAO ‐Penman‐Monteith ( FPM ‐56) equation generally require several meteorological data inputs, which are often unavailable or limited. In the present study, CNN ‐ RNN and GPU ‐accelerated CNN ( CNN ‐ GPU ) models were utilized for temperature‐dependent ET o estimating. ‘ SHapley Additive exPlanations ’ ( SHAP ) analysis revealed that solar radiation and wind speed exerted high degrees of influence, even after their exclusion from the input matrix, which clarified these implicit nonlinear relationships captured by the model. CNN ‐ GPU model outperformed CNN ‐ RNN in both accuracy ( RMSE = 0.23 mm/day, NS = 0.98) and computational efficiency with a faster training time by 20.4%. Despite training with limited input variables (temperature records), the proposed DL ‐based models successfully captured complex temporal and spatial meteorological patterns in the study region.