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In tea garden water resource management, accurately assessing crop water requirements is crucial, with evapotranspiration (ET) serving as a key indicator. The challenges posed by the time series nature, instability, and non-linear coupling in tea garden data were addressed by introducing a novel evapotranspiration prediction model. Firstly, a data processing algorithm, mutual information-principal component analysis (MIPCA), was employed to integrate mutual information (MI) and principal component analysis (PCA), facilitating the selection of features strongly correlated with tea garden transpiration and the extraction of principal components. Subsequently, the temporal convolutional networks (TCN) was integrated with Transformer to construct a new model. Specifically, the grey wolf optimization (GWO) algorithm was employed to optimize the hyperparameters of the TCN, followed by the utilization of the Transformer to capture global dependencies. Ultimately, the two networks were integrated to propose the hybrid model MIPCA-TCN-GWO-Transformer. The model performance was validated through ablation experiments and comparative analyses, while also examining the model’s performance across different time scales. The results showed that the model’s three evaluation indicators such as mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2) were 0.015 mm/d, 0.312 mm/d and 0.962, respectively, which as better than that of traditional prediction models such as long short term memory (LSTM). R2 at hourly scale, daily scale and monthly scale were 0.986, 0.978 and 0.946, respectively, showing good adaptability and accuracy at different time scales. The MIPCA-TCN-GWO-Transformer model constructed had high prediction accuracy and can provide scientific reference for the optimal management of tea garden water resources and the formulation of irrigation systems.