## 4 Results
### Accuracy Evaluation of Different Models  
#### 4.2.1 Accuracy Evaluation at Rain Gauges  
The accuracy evaluations of different precipitation data at the location of rain gauges during the training and testing stages are presented in Table 3. As can be seen from the table, for the training stage, FS-ConvLSTM exhibits superiority over other models in most indicators, except for two evaluation indicators related to total rainfall, including _BIAS_ and _RATIO_, where F-SVD demonstrates better accuracy. For the testing stage, the advantage of FS-ConvLSTM is maintained, but LSTM shows better accuracy in _BIAS_ and _RATIO_. Figure 5: Evaluation of GPM with reference to rain gauge observations. For the evaluation results of the quantitative indicators, the GPM data show an apparent overestimation. The ground-interpolated data slightly underestimate the rainfall, and the accuracy of F-SVD is better than that of IDW in each indicator value since the interpolation accounts for both spatial relationships and the trend of temporal changes. LSTM has lower _BIAS_, and _RATIO_ is closer to 1, while FS-ConvLSTM has lower _RSME_ and higher CC. Through fusing the data from rain gauges, the bias of GPM is improved, and the variation process fits better with the measured values. Compared with GPM, FS-ConvLSTM and LSTM reduce by 63.6% and 46.6% in _RSME_ and improve by 165% and 33.2% in _CC_, respectively. Regarding the evaluation results of the categorical indicators, F-SVD shows obvious advantages over IDW, with higher _POD_ and _TS_ and lower _FAR_ and _MAR_. The performance of LSTM is similar to that of GPM, while FS-ConvLSTM shows a great improvement in each indicator compared to GPM and LSTM. FS-ConvLSTM has the most accurate description of rainfall events, capturing and reflecting more than 60% of the rainfall events while reducing the cases of rainfall misreporting and omission. Compared with GPM, FS-ConvLSTM improves by 22.9% and 62.7% in _POD_ and _TS_ and is reduced by 63.1% and 25.2% in _FAR_ and _MAR_. In summary, FS-ConvLSTM performs optimally in the description of rainfall variation process and event capture capability via the fusion of variation characteristics in the time and space of GPM and ground-interpolated rainfall data. To compare the accuracy of rainfall data at different magnitudes more precisely, an accuracy evaluation was performed separately for light, medium, heavy, and torrential rainfall events during the testing stage, and the results are shown in Table 4. In light rainfall events, F-SVD performs best in the evaluation results of quantitative indicators, showing obvious advantages in _BIAS_, _RSME_, and _RATIO_. GPM performs best in the classification evaluation indicators, with the highest _POD_ and _TS_ and the lowest _MAR_. However, there is an apparent overestimation of rainfall in GPM, with values close to 3.8 times the measured values. In addition, FS-ConvLSTM has the highest _CC_ and the lowest _FAR_ among the models, which also indicates superiority. For the medium, heavy, and torrential rainfall events, FS-ConvLSTM has the lowest _RSME_, _FAR_, _MAR_, and the highest _CC_, _POD_, and _TS_, demonstrating that the spatiotemporal fusion data are closest to the variation process of the measured rainfall series and capture the rainfall events most accurately. Meanwhile, the ground-interpolated data performed best on _BIAS_ and _RATIO_ and are closest to the actual in terms of total rainfall.