Remotely sensed rainfall plays an important role in providing efficient approaches for global or regional rainfall analysis. However, the accuracy of satellite-based products is mainly affected by the errors in sensor observation and retrieval algorithms, particularly with respect to extreme rainfall estimates. The objective of this study is to evaluate the accuracy of satellite-based products in capturing rainfall extremes. The eastern coastal areas of China were chosen as the case study area to compare the accuracy of three mainstream satellite-based products with respect to extreme rainfall estimates during 2003–2015 period. This included the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7, the Climate Prediction Centre Morphing technique RAW (CMORPH-RAW), and the CMORPH bias-corrected product (CMORPH-CRT). In general, all satellite-based products demonstrated numerous errors in extreme rainfall estimates. Based on three different indices of extreme rainfall, it was observed that the satellite-based products underestimated the amounts of rainfall extremes and their respective average values. It was noted that CMORPH-RAW demonstrated the largest relative bias (RB) and underestimated the average extreme rainfall by −31% to −35%. Additionally, all satellite-based products exhibited poor capabilities in capturing the variations in hourly extreme rainfall processes. Finally, a simple potential flood index was developed to simulate the potential flood areas in the eastern coastal areas of China. We found that the potential flood areas can be simulated by combining the potential flood index with the amounts of rainfall derived by satellite-based products.

Satellite-based rainfall products have extensive applications in global change studies, but they are known to contain deviations that require comprehensive verification at different scales. In this paper, we evaluated the accuracies of two high-resolution satellite-based rainfall products: the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7 and the Climate Prediction Center morphing (CMORPH) technique from January 2010 to December 2011 in Shanghai, by using categorical metrics (Probability of Detection, False Alarm Ratio, and Critical Success Index) and statistical indicators (Mean Absolute Error, Root Mean Square Error, Relative Bias, and Correlation Coefficient). Our findings show that, firstly, CMORPH data has a higher accuracy than 3B42V7 at the daily scale, but it underestimates the occurrence frequency of daily rainfall for some intensity ranges. Most errors of the two products are distributed between −10 and 10 mm/day. Second, at the monthly scale, the total accuracy of 3B42V7 is higher than CMORPH in terms of the value of the Correlation Coefficient (CC) and Relative Bias (RB). Finally, CMORPH brings about daily rainfall detection results from categorical metrics computation better than 3B42V7. Generally, the two satellite-based rainfall products show a high correlation with rain gauge data in Shanghai, particularly in spring and winter. Unfortunately, in summer, both of them do not perform well in detecting the short-duration heavy rainfall events. Overall, the relatively poor data accuracy has limited their further applications in Shanghai and similar urban areas.

Global circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact‐related models at local scales. The non‐homogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central‐eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with ~26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%–48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario.