  Precipitation is a key part of hydrological circulation and is a sensitive
indicator of climate change. The Integrated Multi-satellitE Retrievals for the
Global Precipitation Measurement (GPM) mission (IMERG) datasets are widely used
for global and regional precipitation investigations. However, their local
application is limited by the relatively coarse spatial resolution. Therefore,
in this paper, an attention mechanism based convolutional network (AMCN) is
proposed to downscale GPM IMERG monthly precipitation data. The proposed method
is an end-to-end network, which consists of a global cross-attention module, a
multi-factor cross-attention module, and a residual convolutional module,
comprehensively considering the potential relationships between precipitation
and complicated surface characteristics. In addition, a degradation loss
function based on low-resolution precipitation is designed to physically
constrain the network training, to improve the robustness of the proposed
network under different time and scale variations. The experiments demonstrate
that the proposed network significantly outperforms three baseline methods.
Finally, a geographic difference analysis method is introduced to further
improve the downscaled results by incorporating in-situ measurements for
high-quality and fine-scale precipitation estimation.
