Accurate estimation of high-resolution precipitation on the global scale is extremely challenging. The operational Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) has created over 16 years of high-resolution quantitative precipitation estimation (QPE), and has built the foundation for improved measurements in the upcoming Global Precipitation Measurement (GPM) mission. TMPA is intended to produce the “best effort” estimates of quasi-global precipitation from almost all available satelliteborne precipitation-related sensors by consistently calibrating them with the high-quality measurements from the core instrument platform aboard TRMM. Recently, the TMPA system has been upgraded to version 7 to take advantage of newer and better sources of satellite inputs than version 6, and has attracted a large user base. A key product from TMPA is the near-real-time product (TMPA-RT), as its timeliness is particularly appealing for time-sensitive applications such as flood and landslide monitoring. TMPA-RT’s error characteristics on a global scale have yet to be extensively quantified and understood. In this study, efforts are focused on a systematic evaluation of four sets of mainstream TMPA-RT estimates on the global scale. The analysis herein indicates that the latest version 7 TMPA-RT with the monthly climatological calibration had the lowest daily systematic biases of approximately 9% over land and –11% over ocean (relative to the gauge-adjusted research product). However, there still exist some unresolved issues in mountainous areas (especially the Tibetan Plateau) and high-latitude belts, and for estimating extreme rainfall rates with high variability at small scales. These global error characteristics and their regional and seasonal variations revealed in this paper are expected to serve as the benchmark for the upcoming GPM mission.

Similarities and differences of spatial error structures of surface precipitation estimated with successive version 6 (V6) and version 7 (V7) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) algorithms are systematically analyzed through comparison with the China Meteorological Administration's national daily precipitation analysis from June 2008 to May 2011. The TMPA products include V6 and V7 real‐time products 3B42RTV6 and 3B42RTV7 and research products 3B42V6 and 3B42V7. Both versions of research products outperform their respective real‐time counterparts. 3B42V7 clearly improves upon 3B42V6 over China in terms of daily mean precipitation; the correlation coefficient (CC) increases from 0.89 to 0.93, the relative bias (RB) improves from −4.91% to −0.05%, and the root‐mean‐square error (RMSE) improves from 0.69 mm to 0.54 mm. When considering 3 year mean precipitation, 3B42V7 shows similar spatial patterns and statistical performance to 3B42V6. Both 3B42RTV7 and 3B42RTV6 demonstrate similar bias patterns in most regions of China with overestimation by 20% in arid regions (i.e., the north and west of China) and slight underestimation in humid regions (e.g., −5.82% in southern China). However, 3B42RTV7 overestimates precipitation more than 3B42RTV6 in the cold Qinghai‐Tibetan plateau, resulting in a much higher RB of 139.95% (128.69%, 136.09%, and 121.11%) in terms of 3 year annual (spring, summer, and autumn) daily mean precipitation and an even worse performance during winter. In this region, 3B42RTV7 shows an overall slightly degraded performance than 3B42RTV6 with CC decreasing from 0.81 to 0.73 and RB (RMSE) increasing from 21.22% (0.95 mm) to 35.84% (1.27 mm) in terms of daily precipitation.

The performance of Day-1 Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG) and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (3B42V7), was cross-evaluated using data from the best-available hourly gauge network over the Tibetan Plateau (TP). Analyses of three-hourly rainfall estimates in the warm season of 2014 reveal that IMERG shows appreciably better correlations and lower errors than 3B42V7, though with very similar spatial patterns for all assessment indicators. IMERG also appears to detect light rainfall better than 3B42V7. However, IMERG shows slightly lower POD than 3B42V7 for elevations above 4200 m. Both IMERG and 3B42V7 successfully capture the northward dynamic life cycle of the Indian monsoon reasonably well over the TP. In particular, the relatively light rain from early and end Indian monsoon moisture surge events often fails to be captured by the sparsely-distributed gauges. In spite of limited snowfall field observations, IMERG shows the potential of detecting solid precipitation, which cannot be retrieved from the 3B42V7 products.