Over the past several decades, researchers have explored various predictive methods to accurately forecast annual and monthly rainfall for specific regions, recognizing its critical role in water resource management, agriculture, and manufacturing. Reliable precipitation predictions also yield valuable insights into climatological factors and potential future climate change effects. Recent advances in deep learning, particularly recurrent neural network (RNN) architectures, have improved long-range temporal representation learning for rainfall forecasting. However, existing RNN-based models often struggle with delayed or noisy input sequences and fail to adequately capture the relative importance of different input sequences across multiple time steps. To address these limitations, we propose AERNN4RP, a novel RNN framework incorporating multiple levels of attention. AERNN4RP incorporates self-adaptive evolutionary and self-supervising attention mechanisms alongside a sequential auto-encoding strategy to extract complex temporal features while mitigating noise and data uncertainty, thereby enhancing forecasting accuracy. Extensive experiments on real-world rainfall datasets demonstrate the effectiveness and practical benefits of this approach.