As a powerful deep-learning downscaling technique，convolutional neural networks （CNNs） are widely utilized to generate high-resolution precipitation data from low-resolution global climate models through data-driven approaches，playing a critical role in assessing the impacts of climate change at both regional and lo‐ cal scales. This study proposes a CNN-based precipitation downscaling model，the Topography-Guided Attention Network（TGAN），which downscales coarse-resolution（2°）atmospheric variables to produce high-resolution precipitation fields at 0. 1° . The model adopts a Laplacian pyramid as a progressive，multi-level downscaling framework，in which the spatial resolution of precipitation fields is incrementally enhanced and precipitation structures are reconstructed through successive stages. In addition，a topography-guided attention module is in‐ corporated，which leverages an attention mechanism to integrate atmospheric variables with elevation data at cor‐ responding spatial scales. By combining these multi-scale features，the module strengthens the network’s capaci‐ ty for feature representation and learning，thereby improving the accuracy and reliability of simulated precipita‐ tion. Focusing on the middle reaches of the Yellow River，TGAN is trained on daily ERA5 atmospheric vari‐ ables，GPM IMERG daily precipitation data from 2001 to 2010，together with static elevation data，and validat‐ ed using data from 2011 to 2020. The results indicate that，compared with a conventional CNN model，TGAN achieves higher accuracy in spatiotemporal precipitation simulations at daily，monthly，and annual scales. At the daily scale，TGAN achieves a lower average root mean square error（5. 10 mm/day）and a higher average corre‐ lation coefficient（0. 42）compared with the conventional CNN model. Additionally，TGAN more accurately captures extreme precipitation events（95th and 99th percentiles）and better aligns with GPM IMERG observa‐ tions in probability density distribution，particularly for heavy precipitation range. This study further investigates the impact of different loss functions on the downscaling performance of TGAN. Using the RMSE loss function improves overall predictive accuracy but leads to underestimation of extreme precipitation events，whereas the Bernoulli-Gamma loss function，although slightly less accurate overall，more faithfully reproduces extreme pre‐ cipitation events. Its probability density distributions are highly consistent with both GPM IMERG data and sta‐ tion observations，indicating that the model has an enhanced capability to capture extreme precipitation events， thereby better reproducing the distribution characteristics of precipitation frequency. Overall，by combining the topography-guided attention mechanism with the Bernoulli-Gamma loss function，TGAN demonstrates clear ad‐ vantages in downscaling precipitation over the middle reaches of the Yellow River，not only improving overall simulation accuracy but also better representing extreme precipitation events，providing a robust and reliable tool for high-resolution precipitation modeling in complex terrain regions.