## 6 Conclusions  
In this study, GPM and TRMM satellite precipitation products and their feasibility in streamflow simulations was initially evaluated at daily and monthly scales in the data-sparse Chindwin River basin in Myanmar. The main findings of this study are as follows:  
1. In general, IMERG and 3B42V7 represent a similar spatial pattern over the Chindwin River basin, demonstrating a decreasing trend from north to south. IMERG provides a more detailed spatial information of precipitation than 3B42V7, due to its native resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) compared to 3B42V7's 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). 2. Although IMERG and 3B42V7 can capture the temporal variation patterns of daily precipitation at the five rain gauges, these two products still contain considerable errors. IMERG significantly underestimates the total precipitation at all the gauges, and 3B42V7 presents a moderate underestimation at three out of the five gauges. Both products performed poorly in heavy- and light-rain detections and estimations, with a considerable underestimation of heavy-rain estimates and a significant positive bias of light-rain estimates. The accuracy of IMERG and 3B42V7 in estimating monthly precipitation is significantly improved, compared to daily precipitation estimates. Overall, 3B42V7 outperforms IMERG at four out of the five gauges. 3. The large errors in IMERG and 3B42V7 distinctly spread in streamflow simulations via the XAJ hydrological model, with the significant systematic underestimation of total runoff and high flow. The IMERG-based simulations perform worse than those of 3B42V7. The bias correction of satellite precipitation estimates effectively improves the performance of daily and monthly streamflow simulations using IMERG and 3B42V7 data sets. The corrected 3B42V7-based simulations perform slightly better than those using the gauge-based precipitation. In general, IMERG and 3B42V7 are both feasible in streamflow simulations in the Chindwin River basin, with the 3B42V7 product being better suited than IMERG. The above findings provide GPM researchers with timely and useful feedbacks from Myanmar as regards the quality of IMERG products. The newly developed GPM Core sensors and calibration algorithms are not yet able to produce more accurate precipitation estimates than its predecessor, TRMM in the Chindwin River basin. This finding highlights the need for GPM developers to refine the algorithms and improve the accuracy of IMERG products in Myanmar, where plenty of rainfall data are urgently needed for hydrological utilities. Moreover, it emphasizes that local authorities must establish a denser rainfall observation network, which not only facilitates the validation of satellite-derived precipitation but also helps improve the accuracy of streamflow simulations/forecasting for effective flood warning and disaster mitigation. The following are available online at www.mdpi.com/2072-4292/9/3/302/s1, S1: Detailed descriptions of streamflow simulation schemes. Table S1: Physical meaning and sensitivity of the XAJ model parameters and the calibrated parameter values in the area controlled by the Hkami station for the simulation runs using the gauge-based, original 3B42V7 and original IMERG precipitation data sets. Table S2: Statistical indices of the simulated monthly streamflow processes at the five weather stations in the Chindwin River basin (1 April 2014-31 December 2015). Figure S1: Simulated monthly hydrographs using the gauge-based precipitation data, the original 3B42V7 and IMERG precipitation estimates, and their corrected data sets at the river streamflow stations. **Acknowledgments:** This study was sponsored by the National Key Research and Development Program (under Grant No. 2016 YFA0601500) approved by the Ministry of Science and Technology of China; the Special Fund of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (Grand Grant No. 20145031112); the National Natural Science Foundation of China (Grant No.