Data-driven forecasting models are used to understand environmental climatology data better but often overlook missing values and noise, leading to ineffective temporal modeling and inadequate correlations between weather parameters. Consequently, these limitations adversely impact the accuracy of predictions. In response to these issues, a novel deep hybrid model, i.e., a channel attention-enabled bidirectional gated recurrent unit with neural basis expansion analysis for time series (ChAT-BiGRU-NBEATS), is proposed. In this case, a bidirectional gated recurrent unit (Bi-GRU) network is augmented with a channel-attention mechanism and an NBEATS model that facilitates the extraction of complex data features and the prediction of long data sequences. The efficacy of the proposed model is assessed using a comparative analysis against several state-of-the-art deep learning models, utilizing error metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the R2 score. It is evident from the results that the proposed hybrid model surpasses other models in terms of its heightened accuracy.