High‐quality precipitation data are vital for hydrological applications and climate change research. In this study, we evaluated two satellite‐based precipitation products from Chinese Fengyun (FY) 2G (G is one of the third batch of operational geostationary satellites in the Chinese FY‐2 series) and the Global Precipitation Measurement (GPM) against the rain gauge‐based precipitation data, respectively, over the Yunnan‐Kweichow Plateau, China, at different temporal scales (e.g., monthly, daily, and hourly). And the main findings of this study were as follows: (1) FY2G Quantitative Precipitation Estimation (QPE) and Integrated Multisatellite Retrievals for GPM (IMERG) shared similar spatial precipitation patterns with those of rain gauge data, while the FY2G QPE general underestimated the precipitation (bias, −30%–0%), and IMERG obviously overestimated the precipitation (bias, 0%–60%) at monthly scale over the Yunnan‐Kweichow Plateau; (2) the FY2G QPE correlated significantly better (Correlation coefficient, CC, ~0.80) than IMERG (CC ~0.20) against rain gauge data at daily scale, meanwhile, the average daily bias value of IMERG (~30%) was around 6 times larger than that of FY2G QPE (~5%); (3) The FY2G QPE (CC ~0.7, bias ~ − 8%, root‐mean‐square error ~2.0 mm/hr, mean absolute error ~ 0.6 mm/hr) also generally outperformed the IMERG (CC ~0.1, bias ~13%, root‐mean‐square error ~3.0 mm/hr, mean absolute error ~ 1.0 mm/hr), at hourly scale; (4) The FY2G QPE significantly underestimated the precipitation in the period between 16:00 and 20:00 with the probability of detection (POD) decreased from 0.8 to 0.5, while the IMERG significantly overestimated the precipitation in the period between 4:00 and 12:00, at diurnal scale; and (5) one of the reasons resulting the significant overestimations of IMERG might due to its weak abilities in detecting the precipitation events with POD ~0.2 and false alarm ratio (FAR) ~0.7 at diurnal scale, which performed worse than those of the FY2G QPE (POD ~0.8 and FAR ~0.4). These findings would provide valuable recommendations for using these satellite‐based precipitation products in various application fields, such as hydrology, agriculture, and meteorology over the YKP, and also for improving the algorithms of GPM.

The relative effect of climate change and El Niño–Southern Oscillation (ENSO) is essential not only for understanding the hydrological mechanism over Jiangxi province in China but also for local water resources management as well as flood control. This study quantitatively researched in‐depth information on climate change in Jiangxi using the up‐to‐date “ground truth” precipitation and temperature data, the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 1961–2015, 0.25°) data; analyzed the connections between ENSO and climate factors (including precipitation and temperature); and discussed the relationships between the ENSO and climate change. The main findings of this study were (1) during the period of 1961–2015, annual precipitation and temperature generally increased at a rate of 2.68 mm/year and 0.16 °C/10a, respectively; (2) the precipitation temporal trends have significant spatial differences. For example, the high precipitation increasing rates occurred in northern Jiangxi province in summer, while the large decreasing rates happened in most regions of Jiangxi province in spring; (3) an abrupt temperature change was detected around 1984, with general decreasing trends and increasing trends in 1961–1984 and 1984–2015, respectively; (4) ENSO had significant impacts on precipitation changes over Jiangxi province, for example; the El Niño events, beginning in April and May, were likely to enlarge the amounts of precipitation in the following summer, and the El Niño events beginning in October were likely to enlarge the precipitation amounts in the following spring and summer; and (5) the El Niño events, starting in the second half of the year, were likely to raise the temperature in the winter and the following spring. These findings would provide valuable information for better understanding the climate change issues over Jiangxi province.

Precipitation data with fine quality plays vital roles in hydrological-related applications. In this study, we choose the high-quality China Merged Precipitation Analysis data (CMPA) as the benchmark for evaluating four satellite-based precipitation products (PERSIANN-CCS, FY4A QPE, GSMap_Gauge, IMERG-Final) and one model-based precipitation product (ERA5-Land), respectively, at 0.1°, hourly scales over the Zhejiang province, China, in summer, from June to August 2019. The main conclusions were as follows—(1) all other products demonstrate similar patterns with CMPA (~325.60 mm/h, std ~0.07 mm/h), except FY4A QPE (~281.79 mm/h, std ~0.18 mm/h), while, overall, the PERSIANN-CCS underestimates the precipitation against CMPA with a mean value around 236.29 mm/h (std ~0.06 mm/h), and the ERA5-Land, GSMap_Guage, and IMERG-Final generally overestimate the precipitation with a mean value around 370.00 mm/h (std ~0.06 mm/h). (2) The GSMap_Gauge outperforms IMERG-Final against CMPA with CC ~0.50 and RMSE ~1.51 mm/h, and CC ~0.48 and RMSE ~1.64 mm/h, respectively. (3) The PERSIANN-CCS significantly underestimates the precipitation (CC ~0.26, bias ~−35.03%, RMSE ~1.81 mm/h, probability of detection, POD, ~0.33, false alarm ratio, FAR, ~0.47), potentially due to its weak abilities to capture precipitation events and estimate the precipitation. (4) Though ERA5-Land has the best ability to capture precipitation events (POD ~0.78), the largest misjudgments (FAR ~0.54) result in its great uncertainties with CC ~ 0.39, which performs worse than those of GSMap_Gauge and IMERG-Final. (5) The ranking of precipitation products, in terms of the general evaluation metrics, over Zhejiang province is GSMap_Gauge, IMERG-Final, ERA5-Land, PERSIANN-CCS, and FY4A QPE, which provides valuable recommendations for applying these products in various related application fields.

The upper Tarim River basin is supporting about 50 million people by melting the glaciers and snow, which are highly vulnerable and sensitive to climate change. Therefore, assessing the relative effects of climate change on runoff of this region is essential not only for understanding the mechanism of hydrological response over the mountainous areas in Southern Xinjiang but also for local water resources management. This study quantitatively investigated the climate change in the mountainous area of the upper Tarim River basin, using the up-to-date ‘ground-truth’ precipitation and temperature data, the APHRODITE (1961–2010, 0.25°) data; analyzed the potential connections between runoff data, observed at Alar station, and the key climatological variables; and discussed the regression models on simulating the runoff based on precipitation and temperature data. The main findings of this study are: (1) both annual precipitation and temperature generally increases at rates of 0.85 mm/year and 0.25 ℃/10a, respectively, while the runoff data measured at Alar station shows fluctuating decreasing trends; (2) there are significant spatial differences in the temporal trends of precipitation, for example, the larger increasing rates of precipitation occurs in the Karakoram Mountains, while the larger decreasing rates happens in northwestern of Kashgar County; (3) the decreasing trends of temperature mainly occurs in the Kashgar County and its surrounding areas in Summer; (4) seasonal correlations in trends of precipitation and temperature are more significant than those at monthly and annual scale; and (5) the regression model in simulating the runoff in the upper Tarim River basin based on Radial Basis Function (RBF) is better than that based on least-squares method, with the predictive values based on RBF models significantly better (Correlation coefficient, CC, ~ 0.85) than those by least-squares models (CC ~ 0.75). These findings will provide valuable information to inform environmental scientists and planners on the climate change issues in the upper Tarim River basin of Southern Xinjiang, China, under a semiarid-arid cl

In view of the problem that the inversion of soil moisture is greatly affected by vegetation coverage, this paper uses MODIS vegetation products. By eliminating the data of cloud, rain, snow and other quality damage data, the global climate mean data set is compensated by the linear interpolation method after the data average processing. The results showed that NDVI vegetation index changed regularly with seasons, and the change rule was different in different regions. The single pixel interior can accurately reflect the variation trend of the vegetation index of different pixel NDVI in one year. It can describe the seasonal variation of vegetation in the global pixel, reflect the change of green degree, and meet the need to correct the accuracy of vegetation influence.