## 6 Conclusions  
This study proposes an integrated framework (FS-ConvLSTM) to spatiotemporally merge precipitation data from rain gauge observations and GPM (IMERG V06). The proposed framework integrates F-SVD in the recommender system to improve the accuracy of spatiotemporal interpolation based on rain gauge observations, and ConvLSTM merges precipitation data from satellites and rain gauge interpolations by exploiting the spatiotemporal correlation pattern between them. The FS-ConvLSTM framework was applied to estimate the hourly spatial precipitation in the Jianxi Basin of China from 2006 to 2018. The findings are summarized as follows:  
1. The proposed FS-ConvLSTM framework outperforms the comparative models (IDW, F-SVD, and LSTM) in terms of precipitation variation process description and rainfall capture capability, reducing the _RSME_ and _FAR_ of the original GPM data by 63.6% and 63.1%, respectively, and increasing the _CC_ and _POD_ by 165% and 22.9%, respectively.
2. The merged data not only improve the accuracy of the precipitation but also reduce the uncertainty in precipitation estimation. As the intensity of precipitation increases, the precipitation capture ability substantially improves, and the estimation more closely matches the measured data in terms of total rainfall.
3. Due to the powerful feature extraction capability of ConvLSTM, the merged precipitation data combines the advantages of GPM and ground interpolation data with the continuous spatial distribution data and values close to the actual one, and the spatial aggregation of both data is reflected in the fusion.  
The two-step merging framework proposed in this study demonstrates satisfactory performance in merging hourly spatial precipitation data in the Jianxi basin of China by exploring the spatiotemporal dependence between rain gauge and GPM. Different types of precipitation data, such as Tropical Rainfall Measuring Mission (TRMM), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), possess their unique characteristics and advantages. In this study, we only considered merging data from rain gauges and GPM. To maximize the benefits of each satellite product's data and enhance the accuracy of spatial precipitation estimations, future work should consider incorporating more multi-source precipitation observation data.  
Conceptualization, S.S. and H.C.; methodology, S.S., H.C. and K.L.; software, S.S. and K.L.; validation, S.S., N.Z. and B.T.; formal analysis, S.S.; investigation, N.Z. and B.T.; resources, S.S. and K.L.; data curation, N.Z., B.T. and K.L.; writing--original draft preparation, S.S.; writing--review and editing, H.C. and C.-Y.X.; visualization, S.S.; supervision, H.C. and C.-Y.X.; project administration, K.L.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program (2022 YFC3002701) and Water Science and Technology Project in Fujian Province, China.  
The rain gauge observation data used in this study are confidential and the GPM (IMERG V06) Final Run data were downloaded from NASA ([[https://earthdata.nasa.gov/](https://earthdata.nasa.gov/)]([https://earthdata.nasa.gov/](https://earthdata.nasa.gov/)); accessed on 15 October 2022).