based on GPCC to daily‐scale adjustment because (1) the monthly‐scale adjustment has limited effect on metrics such as CC, RMSE, and CSI at the hourly scale, (2) IMERG_uncal shows similar performance with IMERG_cal in detecting precipitation occurrence at daily and hourly scales, and (3) GSMaP outperforms IMERG, benefiting from its daily‐scale gauge adjustment. Moreover, IMERG shows weak capability in estimating precipitation under cold climate (e.g., during winter and over high latitudes) due to the effect of snowfall, which could be improved through better rainfall‐snowfall classification or physically based retrieval algorithms. The trend of snowfall is most notable in TP, whereas different data sets show contradictory trends. The measurement or estimation of snowfall remains a challenging problem.
APPENDIX: ABBREVIATIONS
| Abbreviation | Full phrase |
|---|---|
| CC | Pearson correlation coefficient |
| CGDPA | Chinese Rain Gauge‐based Daily Precipitation Analysis |
| CHIRPS | Climate Hazards group Infrared Precipitation with Stations |
| CMA | China Meteorological Administration |
| CMORPH | Climate Prediction Center (CPC) MORPHing technique bias corrected (CRT) |
| CPC | Climate Prediction Center |
| CR | critical rainfall |
| CSI | Critical success index |
| ERA‐Interim | ECMWF ReAnalysis Interim |
| ERA5 | Fifth generation of ECMWF atmospheric reanalyses of the global climate |
| FAR | False alarm ratio |
| FFG | Flash Flood Guidance |
| GPM | Global Precipitation Measurement |
| GSMaP | Global Satellite Mapping of Precipitation |
| GSMaP | Gauge‐adjusted Global Satellite Mapping of Precipitation V6/V7 |
| IMERG | Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement |
| IMERG_cal | IMERG calibrated precipitation |
| IMERG_uncal | IMERG uncalibrated precipitation |
| IMERG‐E | IMERG Early run |
| IMERG‐F | IMERG Final run |
| KGE’ | Kling‐Gupta efficiency |
| ME | Mean error |
| MERRA2 | The Modern‐Era Retrospective Analysis for Research and Applications, Version 2 |
| MTC | Multiplicative TC |
| NE | Northeastern region |
| PCDR | PERSIANN‐Climate Data Record |
| PERSIANN | Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks |
| PERSIANN‐CCS | PERSIANN‐ Cloud Classification System |
| POD | Probability of detection |
| RMSE | Root mean square error |
| RTI | Rain Trigger Index |
| SM2RAIN | SM2RAIN based on ESA Climate Change Initiative (CCI) |
| SMI | Soil Moisture Index |
| T3B42 | TRMM Multi‐satellite Precipitation Analysis (TMPA) 3B42 V7 |
| TC | Triple collocation |
| TMI | TRMM microwave imager |
| TMPA | TRMM multi‐satellite precipitation analysis |
| TP | Qinghai‐Tibet Plateau |
| XJ | Xinjiang Province |
ACKNOWLEDGMENTS
We appreciate the extensive efforts by the developers of the ground, satellite, and reanalysis precipitation datasets to make their products available. The study is funded by the Global Water Futures program in Canada, the National Natural Science Foundation of China (grant 71461010701 and 41471430), and the National Key R&D Program of China (2018YFC1508105).
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