• A comprehensive review and analysis of IMERG validation studies from 2016 to 2019. • There is robust representation of spatio-temporal patterns of precipitation. • Discrepancies can be found in extreme and light precipitation, and the winter season. • The 30-min scale has not yet been sufficiently evaluated. • Using IMERG in hydrological simulation results to high variance in their performance. Accurate, reliable, and high spatio-temporal resolution precipitation data are vital for many applications, including the study of extreme events, hydrological modeling, water resource management, and hydroclimatic research in general. In this study, we performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe. Asia, and in particular China, are the subject of the largest number of IMERG evaluation studies on the continental and country level. When compared to ground observational records, IMERG is found to vary with seasons, as well as precipitation type, structure, and intensity. It is shown to appropriately estimate and detect regional precipitation patterns, and their spatial mean, while its performance can be improved over mountainous regions characterized by orographic precipitation, complex terrains, and for winter precipitation. Furthermore, despite IMERG's better performance compared to other satellite products in reproducing spatio-temporal patterns and variability of extreme precipitation, some limitations were found regarding the precipitation intensity. At the temporal scales, IMERG performs better at monthly and annual time steps than the daily and sub-daily ones. Finally, in terms of hydrological application, the use of IMERG has resulted in significant discrepancies in streamflow simulation. However, and most importantly, we find that each new version that replaces the previous one, shows substantial improvement in almost every spatiotemporal scale and climatic condition. Thus, despite its limitations, IMERG evolution reveals a promising path for current and future applications.

The Köppen-Geiger (KG) climate classification has been widely used to determine the climate at global and regional scales using precipitation and temperature data. KG maps are typically developed using a single product; however, uncertainties in KG climate types resulting from different precipitation and temperature datasets have not been explored in detail. Here, we assess seven global datasets to show uncertainties in KG classification from 1980 to 2017. Using a pairwise comparison at global and zonal scales, we quantify the similarity among the seven KG maps. Gauge- and reanalysis-based KG maps have a notable difference. Spatially, the highest and lowest similarity is observed for the North and South Temperate zones, respectively. Notably, 17% of grids among the seven maps show variations even in the major KG climate types, while 35% of grids are described by more than one KG climate subtype. Strong uncertainty is observed in south Asia, central and south Africa, western America, and northeastern Australia. We created two KG master maps (0.5° resolution) by merging the climate maps directly and by combining the precipitation and temperature data from the seven datasets. These master maps are more robust than the individual ones showing coherent spatial patterns. This study reveals the large uncertainty in climate classification and offers two robust KG maps that may help to better evaluate historical climate and quantify future climate shifts.

Like civilization and technology, our understanding of the global water cycle has been continuously evolving, and we have adapted our quantification methods to better exploit new technological resources. The accurate quantification of global water fluxes and storages is crucial in studying the global water cycle. These fluxes and storages physically interact with each other, are related through the water budget, and are constrained by it. First attempts to quantify them date back to the early 1900s, and during the past few decades, they have received an increasing research interest, which is reflected in the vast amount of data sources available nowadays. However, these data have not been comprehensive enough due to the high spatiotemporal variability of the global water cycle. Herein, we provide a comprehensive review of the chronological evolution of global water cycle quantification, the distinct data sources and methods used, and a critical assessment of their contribution to improving the spatiotemporal monitoring of the global water cycle. The chronology of global water cycle components shows that the uncertainty of flux estimates over oceans remains higher than that over land. Comparing the standard deviation and the interquartile range of the estimates from the 2000s onward with those from all the estimates (1905-2019), we can affirm that statistical variability has diminished in recent years. Moreover, the variability of ocean precipitation and evaporation estimates from the 2000 onward was reduced by more than 70% compared with earlier studies. These findings advocate that the consistency of global water cycle quantification has been improved.

Abstract Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system processes at different spatiotemporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. Here, we propose a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatiotemporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatiotemporal continuum. The proposed cross-scale framework for integrating multi-source data in Earth system sciences is already developed as a stand-alone R package that is freely available to download.

The change in the empirical distribution of future global precipitation is one of the major implications regarding the intensification of global water cycle. Heavier events are expected to occur more often, compensated by decline of light precipitation and/or number of wet days. Here, we scrutinize a new global, high‐resolution precipitation data set, namely, the Multi‐Source Weighted‐Ensemble Precipitation v2.0, to determine changes in the precipitation distribution over land during 1979–2016. To this end, the fluctuations of wet days precipitation quantiles on an annual basis and their interplay with annual totals and number of wet days were investigated. The results show increase in total precipitation, number of wet days, and heavy events over land, as suggested by the intensification hypothesis. However, the decline in light/medium precipitation or wet days was weaker than expected, debating the “compensation” mechanism.