This study simultaneously analyzed and evaluated the meteorological drought-monitoring utility of the following four satellite-based, quantitative precipitation estimation (QPE) products: the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B43V7 (TRMM-3B43), the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), the Climate Prediction Center Morphing Technique gauge-satellite blended product (CMORPH-BLD), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). Data from 2000 to 2016 was used at global scale. The global Climate Research Unit (CRU) Version 4.02 was used as reference data to assess QPE products. The Standardized Precipitation Evapotranspiration Index (SPEI) drought index was chosen as an example to evaluate the drought utility of four QPE products. The results indicate that CHIRPS has the best performance in Europe, Oceania, and Africa; the PERSIANN-CDR has the best performance in North America, South America, and Asia; the CMORPH-BLD has the worst statistical indices in all continents. Although four QPE products showed satisfactory performance for most of the world according to SPEI statistics, poor drought monitoring ability occurred in Southeast Asia, Central Africa, the Tibetan plateau, the Himalayas, and Amazonia. The PERSIANN-CDR achieves the best performance of the four QPE products in most regions except for Africa; CHIRPS and TRMM-3B43 have comparable performances. According to the spatial probability of detection (POD) and false alarm ratio (FAR) of the SPEI, more than 50% of all drought events cannot be accurately identified by QPE products in regions with sparse gauge distribution. In other regions, such as the southeastern USA, southeastern China, and South Africa, QPE products capture more than 75% of drought events. Temporally, all datasets (except for CMORPH-BLD) can detect all typical drought events, namely, in the southeastern US in 2007, western Europe in 2003, Kenya in 2006, and Central Asia in 2008. The study concludes that CHIRPS and TRMM-3B43 can be used as near-real-time drought monitoring techniques whereas PERSIANN-CDR might be more suitable for long-term historical drought analysis.

The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Run (IMERGF) product has now been upgraded to Version 4 (V4), which has been available since March 2017. Therefore, it is desirable to evaluate the characteristic differences between the V4 and the previous V3 products. A comprehensive performance evaluation of the errors of the successive V3 and V4 IMERGF products is performed with a comparison of the China daily Precipitation Analysis Products (CPAP) from March 2014 to February 2015. The version 6 Global Satellite Mapping of Precipitation (GSMaP) research product (which is another Global Precipitation Measurement (GPM) based precipitation product) is also used as a comparison in this study. Overall, the IMERGF-V4 product does not exhibit the anticipated improvement for China compared to the IMERGF-V3 product. An analysis of the metrics of annual daily average precipitation over China for the IMERGF-V3 and IMERGF-V4 products indicates a decrease of the relative bias (RB) from 3.70% to −7.18%, a decrease of the correlation coefficient (CC) from 0.91 to 0.89, an increase of the fractional standard error (FSE) from 0.49 to 0.56, and an increase of the root-mean-square error (RMSE) from 0.63 mm to 0.72 mm. Compared to the IMERGF-V3 product, the IMERGF-V4 product exhibits a significant underestimation of precipitation in the Qinghai-Tibetan plateau with a much lower RB of −60.91% (−58.19%, −65.30%, and −63.74%) based on the annual (summer, autumn, and winter) daily average precipitation and an even worse performance during winter (−72.33% of RB). In comparison, the GSMaP product outperforms the IMERGF-V3 and IMERGF-V4 products and has the smallest RMSE (0.47 mm/day), highest CC (0.95), lowest FSE (0.37), and best performance of the RB (−2.39%) in terms of annual daily precipitation over China. However, the GSMaP product underestimates the precipitation more than the IMERGF-V3 product for the arid XJ region.

Drought and floods affect the growth and yield of maize, affecting food security. Therefore, it is crucial to assess maize’s drought and waterlogging status in various growth stages. We used phenological and daily meteorological data and spatial analysis to identify the drought and waterlogging conditions of spring maize in Northeast China in eight growth stages. We calculated the crop water surplus/deficit index and used the national standard for maize drought and waterlogging. The results indicate a significant decreasing trend of effective precipitation in Northeast China. The maize’s water requirements changed during the growing period. The ranking of the daily water requirements of maize from high to low in the different growth stages was the flowering stage to the silking stage (6.9 mm/d), the tasseling stage to the flowering stage (6.1 mm/d), the jointing stage to the tasseling stage (4.9 mm/d), the seven-leaf stage to the jointing stage (3.4 mm/d), the silking stage to the harvesting stage (2.0 mm/d), the emergence stage to the three-leaf stage (1.4 mm/d), the three-leaf stage to the seven-leaf stage (1.3 mm/d), and the sowing stage to the emergence stage (1.2 mm/d). Drought occurred primarily in the early growth and development stage, and the most severe drought conditions were observed in the sowing to emergence stages and the emergence to the three-leaf stages in most areas in Northeast China. Waterlogging occurred predominantly in the flowering to the silking stages and the silking to the maturity stages in southeast Liaoning and parts of Jilin. Inner Mongolia had the lowest soil moisture conditions and was unsuitable for maize growth, followed by Heilongjiang, Jilin, and Liaoning. The dominant meteorological factors affecting the drought and waterlogging status of maize in different growth stages were precipitation and wind speed, followed by the minimum temperature, relative humidity, sunshine hours, and maximum temperature. The average temperature did not influence the drought and waterlogging status. The results provide a basis for selecting drought-resistant varieties and preventing waterlogging.

The Northeast region of China and Huang Huai Hai (3H) region are vital maize production bases in northern China that are crucial for national food security. The absence of phenological data hinders a detailed assessment of the alignment between maize development stages and climatic resources. This study combines the authors’ maize phenology data with climate suitability modeling to evaluate maize’s climate suitability at different developmental stages in both regions. This study shows that during the maize growth cycle, the average temperature, precipitation, sunshine, and comprehensive climate suitability were 0.77, 0.49, 0.87, and 0.65, respectively, in the Northeast. In contrast, the average temperature, precipitation, sunshine, and comprehensive climate suitability in the 3H region were 0.98, 0.53, 0.73, and 0.70, respectively. Precipitation is a major factor influencing maize growth, with temperature and sunshine impacting growth differently across regions. Temperature significantly affects maize in the Northeast, while sunshine plays a greater role in the 3H region. The Northeast is suitable for drought-resistant maize varieties, and implementing a late harvest policy in Liaoning could enhance maize yield. The 3H region generally has favorable climatic conditions. Apart from certain parts of Henan needing drought-resistant varieties, areas with ample growing seasons can adopt long-duration varieties to maximize thermal resource utilization. Our results have important implications for optimizing maize planting strategies and enhancing regional resilience, aiming to assess meteorological factors’ impact on maize growth in key production areas.