Abstract: Sea surface temperature (SST) is an important geophysical parameter to study global climate change. Accurate prediction of SST is of great significance to global climate change, marine environment and fishery development. In order to improve prediction accuracy of SST, the HDC-BiGRU hybrid model with attention mechanism (HDC-BiGRU-AT) is proposed by extracting the temporal and spatial characteristics to predict the sea surface temperature in the next seven days. The model consists of the encoder and the decoder. In the encoding phase, hybrid dilated convolution (HDC) can extract spatial characteristics of SST, and bidirectional gated recurrent neural network (BiGRU) can capture temporal characteristics of SST. By adding attention mechanism to assign different weights to the output information and assign higher weight coefficients to the important information, the information encoding is realized and the prediction accuracy of the model is improved in the decoding stage. Two-dimensional SST data selected from the East and South China Seas are adopted for modeling. The experimental results show that the values of error indices on the HDC-BiGRU-AT model are lower than those of the existing models, which verifies the feasibility and effectiveness of the proposed method.