The accurate knowledge of precipitation information over the Qinghai-Tibet Plateau, where the rain gauge networks are limited, is vital for various applications. While satellite-based precipitation estimates provide high spatial resolution (0.25°), large uncertainties and systematic anomalies still exist over this critical area. To derive more accurate monthly precipitation estimates, a spatial data-mining algorithm was used to remove the obvious anomalies compared with their neighbors from the original Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) 3B43 V7 data at an annual scale, as the TMPA data are more accurate than other satellite-based precipitation estimates. To supplement the international exchange stations, additional ground observations were used to calibrate and improve the TMPA data with anomalies removed at an annual scale. Finally, a disaggregation strategy was adopted to derive monthly precipitation estimates based on the calibrated TMPA data. We concluded that: 1) the obvious anomalies compared with their neighbors could be removed from the original TMPA data sets and 2) the calibrated results were of a higher quality than the original TMPA data in each month from 2000 to 2013. The improved TMPA 3B43 V7 data sets over the Qinghai-Tibet plateau, named NITMPA3B43_QTP, are available at http://agri.zju.edu.cn/NITMPA3B43_QTP/.