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Accurate knowledge on spatiotemporal characteristics of historical precipitation extremes could provide great potential guidance for preventing hydrological‐related disasters caused by precipitation extremes in the future. On the basis of the fifth generation of atmospheric reanalysis precipitation data by the European Centre for Medium Range Weather Forecasts (ERA5, 0.25°, 1 hourly, 1950–2020) with high spatiotemporal resolutions, continuity and quality, this study analyzed the spatiotemporal characteristics of precipitation extremes over the Ganjiang River Basin and its surroundings during 1950–2020. The main conclusions include, but are not limited to, the following: (1) In general, precipitation extremes present increasing trends over most areas of the basin and its surroundings. For instance, areas showing upward trends of R10, SDII and PRCPTOT account for ~93.45%, ~66.36%, and ~88.18%, respectively. (2) The spatiotemporal variations of precipitation extremes over the Ganjiang River Basin and its surroundings show obvious northwest–southeast differences. For instance, precipitation extremes are increasing in the southeastern parts, but they are decreasing in the northwestern parts. (3) High‐value clusters are also identified in the southeast (e.g., R10, SDII, R95P and PRCPTOT, accounting for ~20.71%, ~20.72%, ~25.88%, and ~22.56%, respectively) and low‐value clusters in the northwest (e.g., Rx5day, SDII and R95P, accounting for ~18.05%, ~27.03%, and ~21.18%, respectively). (4) The spatiotemporal variations of precipitation extremes in both the southeast and northwest are quite stable. For example, regions with less than five abrupt change points of R10, SDII, and PRCPTOT account for 77.49%, 54.84%, and 81.74%, respectively. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is characterized by complex topography and heterogeneous surface cover, which makes it difficult to obtain accurate precipitation data at a regional scale. Therefore, it is important to use dense ground observations to evaluate satellite‐derived precipitation products. In this study, the top‐down‐based Global Precipitation Measurement Integrated MultisatellitE Retrievals (GPM IMERG) and bottom‐up‐based Soil Moisture TO RAIN‐Advanced SCATterometer (SM2RAIN‐ASCAT; SM2RASC) precipitation products were evaluated against 1,584 unevenly distributed rain gauges over the TP from May to October 2015. Results show: (1) The overall performances of IMERG and SM2RASC are comparable at the daily scale, with a higher correlation coefficient (CC) for IMERG and lower root‐mean‐square error (RMSE) for SM2RASC. Regarding the precipitation detectability, IMERG outperforms SM2RASC; (2) Spatially, SM2RASC shows better performance than IMERG in grasslands, but IMERG outperforms SM2RASC in forest areas, due to the low quality of the soil moisture retrieved by satellite in the areas; (3) The accuracy of both SM2RASC and IMERG is closely correlated with altitude and rainfall intensity. The SM2RASC product estimates light to moderate rainfall (5–25 mm/day) more accurately than the IMERG, but the IMERG product shows better performance for drizzle and heavy rainfall (>25 mm/day). Due to relatively frequent fluctuations of the ASCAT soil moisture product, SM2RASC frequently reports false rainfall in dry conditions. This study demonstrates the good performance of the SM2RASC product over the TP and suggests that the impact of vegetation density and topography should be considered for improving the SM2RAIN algorithm. Key Points: Soil Moisture TO RAIN‐Advanced SCATterometer (SM2RAIN‐ASCAT) estimates daily rainfall better in grassland areas, and the Global Precipitation Measurement‐Integrated MultisatellitE Retrievals for GPM (GPM IMERG) product outperforms in forestsSM2RAIN‐ASCAT estimates light to moderate rainfall more accurately than GPM IMERG, but it cannot detect heavy rainfall eventsDue to relatively frequent fluctuations of ASCAT soil moisture product, SM2RAIN‐ASCAT frequently reports false rainfall in dry conditions [ABSTRACT FROM AUTHOR]

Precipitation phase has an important influence on hydrological processes. The Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) uses temperature data from reanalysis products to implement rain–snow classification. However, the coarse resolution of reanalysis data may not reveal the spatiotemporal variabilities of temperature, necessitating appropriate downscaling methods. This study compares the performance of eight air temperature Ta downscaling methods in the contiguous United States and six mountain ranges using temperature from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) as the benchmark. ERA-Interim Ta is downscaled from the original 0.75° to 0.1°. The results suggest that the two purely statistical downscaling methods [nearest neighbor (NN) and bilinear interpolation (BI)] show similar performance with each other. The five downscaling methods based on the free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of Ta. The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature Tw, for rain–snow classification, using Ta and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate Tw compared to NN and BI in the western United States, whereas the improvement is limited in the eastern United States. Rain–snow partitioning is conducted using a critical threshold of Tw with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based Tw using TLR downscaling methods is better than that using NN/BI and IMERG precipitation phase. In conclusion, TLR-based downscaling methods show promising prospects in acquiring high-quality Ta and Tw with high resolution and improving rain–snow partitioning, particularly in mountainous regions. [ABSTRACT FROM AUTHOR]

Compared with traditional methods to investigate spatial and temporal precipitation patterns over mountainous regions with limited meteorological observations, satellite-based precipitation estimates provide much more information to aid in understanding spatial distribution and variability, especially on the Qinghai-Tibetan Plateau and surrounding regions (TP), where the precipitation climate has been insufficiently documented so far. The monthly Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B43 V7 datasets, with high resolution (0.25°), were firstly applied in this study to investigate the spatial and temporal precipitation patterns over the TP, from 1998 to 2013. We found that the mean precipitation at the TP scale did not show clear trends at monthly, seasonal, or annual scales. We also found that precipitation over the TP shows different spatial variation trends at different temporal scales. For example, along the southern Himalayas, there is a clearly decreasing annual precipitation trend, with rates ranging from −120 to −20 mm/year, and the precipitation increases along the west coast of India to eastern Pamir, with rates larger than 20 mm/year. Another finding was that the precipitation climate over the TP is influenced by different atmospheric circulation patterns; the contribution of monthly and seasonal precipitation to total annual precipitation varies spatially. For instance, the annual precipitation along the southern Himalayas and the southeast TP mainly occurs in summer, and the northwest TP mainly receives precipitation in spring and winter. [ABSTRACT FROM AUTHOR]

• APHRODITE, CHIRPS, PERSIANN-CDR were evaluated over TP. • Performance of gridded products varied in precipitation occurrences and amount. • APHRODITE performed the best and with longest time span (>50 years). • PERSIANN-CDR outperformed CHIRPS in detecting rainfall occurrences. Long-term precipitation data play significant roles in hydrological applications and climate change studies. In this study three gridded precipitation products (Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources, APHRODITE; Climate Hazards Group InfraRed Precipitation with Station data, CHIRPS; and Precipitation Estimation from Remotely sensed Information using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR) with the spatial resolution of 0.25° were evaluated using rain gauge data at the daily, monthly and annual scales over the Tibetan Plateau and its surrounding areas (TP). R2, bias, RMSE and MAE were adopted to assess precipitation amount; indices including the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) and frequency bias index (FBI) were used to evaluate gridded products' ability to detect precipitation occurrences. We found that: (1) APHRODITE outperformed CHIRPS and PERSIANN-CDR against observations at the annual scale with the highest R2 (~0.93), the lowest MAE (~67 mm/year) and RMSE (~116 mm/year), and APHRODITE also showed better performance in distinguishing rain/no rain events with the highest POD (~0.9) and CSI (>0.6), the lowest FAR (~0.35), followed by PERSAINN-CDR and CHIRPS; (2) CHIRPS performed nearly to APHRODITE in precipitation amount with R2 around 0.8 while the lowest POD (<0.4) implied the shortage for CHIRPS in detecting rain/no rain conditions; (3) in spite of the lowest agreement with gauge measurements with the largest bias (~25%), PERSIANN-CDR had a relatively higher mean POD (>0.5), indicating that PERSIANN-CDR had better potential of detecting rainfall occurrences than CHIRPS but tended to overestimate precipitation; (4) generally, PERSIANN-CDR overestimated the precipitation greater than CHIRPS and APHRODITE data at monthly scale; and (5) the three products correlated well with each other in the interior, southern and eastern regions of TP but there were still some significant differences along the borders and surroundings of the TP by inter-comparisons. Finally, we recommended APHRODITE for climate change study for its high accuracy and long-time series, CHIRPS and PERSIANN-CDR could also be considered after correction. [ABSTRACT FROM AUTHOR]

• First exploration on downscaling IMERG at hourly scale in typhoon event. • Proposed a new downscaling strategy based on moving window technology. • Cloud properties from Himawari 8 introduced for downscaling IMERG. • Downscaled results outperformed the original IMERG data. Accurate gridded precipitation products with both finer tempo-spatial resolutions are critical for various scientific communities (e.g., hydrology, meteorology, climatology, and agriculture). Downscaling on coarse satellite-based rainfall estimates is an optimal approach to obtain such datasets. The Integrated Multi-satellitE Retrivals for Global Precipitation Measurement (GPM) (IMERG) data provides the "best" satellite-based precipitation estimates at half-hourly/0.1° scales, while its spatial resolution is still coarse for certain hydrometeorology research. To acquire hourly downscaled precipitation estimates based on IMERG, there are two great challenges: (1) limited rainfall-related environmental variables (0.01°×0.01°, hourly) used to downscale the IMERG data; and (2) far few rainfall pixels used for regressing traditional relationships between precipitation and environmental variables. In this case, most traditional or commonly-used regression/empirical models and the state-of-art machine learning algorithms are not suitable to cater these requirements. Therefore, we proposed a new strategy to obtain hourly downscaled precipitation estimates based on IMERG called Geographically Moving Window Weight Disaggregation Analysis (GMWWDA). Additionally, we explored multiple cloud properties, including cloud effective radius (CER), cloud top height (CTH), cloud top temperature (CTT) and cloud optical thickness (COT), as covariates to downscale IMERG using GMWWDA method, and concluded as follows: (1) the downscaled results (0.01° × 0.01°, hourly) based on the above mentioned cloud properties outperformed the original IMERG data; (2) the downscaled results based on CER performed better than those based on CTH, CTT and COT, respectively; (3) the accuracy of satellite-based precipitation products pose significant effects on those of the downscaled results. This study provides a great potential solution for generating satellite-based precipitation dataset with finer spatio-temporal resolutions. [ABSTRACT FROM AUTHOR]

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. Key Points: Evaluations on Chinese FY2G QPE and GPM IMERG precipitation data over the YKP, ChinaFY2G QPE significantly underestimated the precipitation from 16:00 to 20:00 and IMERG overestimated the precipitation from 4:00 to 12:00The overestimations of IMERG might greatly due to its weak abilities in detecting the precipitation events [ABSTRACT FROM AUTHOR]

Long-term precipitation estimates with both finer spatial resolution and better quality are vital and highly needed in various related fields. Numerous downscaling algorithms have been investigated based on the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), to obtain precipitation data with finer resolution (~1 km). However, this research was restricted by the time span of the TMPA dataset, as the starting time of TMPA was 1998. In this study, a new methodological framework incorporating wavelet coherence and Cubist was proposed to retrospectively obtain downscaled precipitation estimates (DS) over the Tibetan Plateau (TP), based on TMPA and ground observations, in 1990s. The correlations and similarities of precipitation patterns between the target years, from 1990 to 1999, and reference years, from 2000 to 2013, were firstly determined using wavelet coherence based on ground observations. Following this, the TMPA data in the reference years were regarded as the reference in the corresponding target years, which were adopted to be downscaled using Cubist models and land surface variables, to obtain the DS in the target years. We found that the DS showed continuous trends, which corresponded well with the ground observations. Additionally, the performances of the DS were better than those of the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data over the TP. Therefore, this methodological framework has great potential for obtaining precipitation estimates for the period of the 1990s for which TMPA data is inaccessible. [ABSTRACT FROM AUTHOR]

Rainfall information is a prerequisite to and plays a vital role in driving hydrological models. However, limited by the observation methods, the obtained precipitation data, at present, are still too coarse. In this study, a new downscaling method was proposed to obtain high spatial resolution (~1 km/hourly) precipitation estimates based on Integrated Multi-satellitE Retrievals for GPM (IMERG) data at hourly scale. Compared with original IMERG data, the downscaled precipitation results showed the similar spatial patterns with those of original IMERG data, but with finer spatial resolution. In addition, the downscaled precipitation estimates were further analyzed to quantify their improvements using the Coupled Routing and Excess STorage (CREST) model across Ganjiang River basin. Compared with the observed streamflow, the downscaled precipitation results showed satisfying hydrological performance, with Nash-Sutcliffe Coefficient of Efficiency (NSCE), Root Mean Square Error (RMSE), Relative Bias (BIAS), and Correlation Coefficient (CC). The improvement in terms of four statistic metrics in terms of streamflow simulation also indicated great potential of hydrological utility for the downscaled precipitation results. [ABSTRACT FROM AUTHOR]

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) produces the latest generation of satellite precipitation estimates and has been widely used since its release in 2014. IMERG V06 provides global rainfall and snowfall data beginning from 2000. This study comprehensively analyzes the quality of the IMERG product at daily and hourly scales in China from 2000 to 2018 with special attention paid to snowfall estimates. The performance of IMERG is compared with nine satellite and reanalysis products (TRMM 3B42, CMORPH, PERSIANN-CDR, GSMaP, CHIRPS, SM2RAIN, ERA5, ERA-Interim, and MERRA2). Results show that the IMERG product outperforms other datasets, except the Global Satellite Mapping of Precipitation (GSMaP), which uses daily-scale station data to adjust satellite precipitation estimates. The monthly-scale station data adjustment used by IMERG naturally has a limited impact on estimates of precipitation occurrence and intensity at the daily and hourly time scales. The quality of IMERG has improved over time, attributed to the increasing number of passive microwave samples. SM2RAIN, ERA5, and MERRA2 also exhibit increasing accuracy with time that may cause variable performance in climatological studies. Even relying on monthly station data adjustments, IMERG shows good performance in both accuracy metrics at hourly time scales and the representation of diurnal cycles. In contrast, although ERA5 is acceptable at the daily scale, it degrades at the hourly scale due to the limitation in reproducing the peak time, magnitude and variation of diurnal cycles. IMERG underestimates snowfall compared with gauge and reanalysis data. The triple collocation analysis suggests that IMERG snowfall is worse than reanalysis and gauge data, which partly results in the degraded quality of IMERG in cold climates. This study demonstrates new findings on the uncertainties of various precipitation products and identifies potential directions for algorithm improvement. The results of this study will be useful for both developers and users of satellite rainfall products. • GPM IMERG and nine precipitation products are evaluated from 2000 to 2018. • IMERG shows higher accuracy by years due to increasing passive microwave samples. • Some new findings of various precipitation datasets are demonstrated and discussed. • The accuracy, distributions, and trends of snowfall in China are revealed. • Reanalysis products exceed IMERG and even gauge data in snowfall estimation. [ABSTRACT FROM AUTHOR]