High-resolution Precipitation Data Research Articles

Evaluation of gridded precipitation datasets in mountainous terrains of Northwestern Mexico

Study regionThe complex mountainous terrains of the Sierra Madre Occidental in northwestern Mexico. Study focusAcquiring high-resolution precipitation data in regions with limited conventional rain gauge coverage poses significant challenges. Gridded precipitation (GP) datasets, including gauge-based, satellite, and reanalysis products, provide a potential solution, but their reliability in areas with complex terrain and intricate precipitation patterns remains uncertain. This study comprehensively evaluates the performance of four GP datasets—AORC, CHIRPS, Daymet, and ERA5—in estimating precipitation. The evaluation was conducted at a daily, monthly, and seasonal timescale, further analyzing extreme precipitation, the influence of elevation, and spatial averaging across hydrologic basins, using as reference the NERN rain gauge data from 2002 to 2004. New hydrological insights for the regionResults indicate that Daymet and AORC are the most accurate GP datasets for daily and monthly timescales, respectively. All datasets improve in accuracy over longer timescales but face challenges during the wet summer monsoon months and extreme events, with Daymet performing relatively better. Terrain elevation had a minimal impact on overall dataset accuracy, though a slight improvement in precipitation detection was noted as elevation increased. This work provides valuable insights into the strengths and limitations of GP datasets in regions with complex terrain and orographically-forced convective precipitation, offering practical outcomes for climate and hydrologic studies in similar regions.

PreciDBPN: A customized deep learning approach for hourly precipitation downscaling in eastern China

Long-term series of high-resolution gridded precipitation datasets are essential for hydrological and meteorological research. Producing high-resolution precipitation data from regional models demands substantial computational resources and labor. Global Reanalyses offer long-term coverage and effectively capture annual and seasonal precipitation patterns. However, they have inadequate resolution and frequently have difficulties depicting extreme conditions. This study proposes an efficient and accurate approach for generating long-term series of high spatial and temporal resolution precipitation. It is achieved by leveraging deep learning techniques to integrate the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) global climate reanalysis (ERA5, 0.25°, hourly) data with high-resolution precipitation fusion datasets. Considering the heavy-tailed distribution of precipitation, we developed the PreciDBPN model structure, which combines a classification network with a super-resolution network and incorporates physically relevant indices into the model's input. We trained and evaluated the PreciDBPN and baseline models in eastern China using the China Meteorological Administration Land Data Assimilation System (CLDAS) precipitation dataset (0.0625°, hourly, 2017–2022). When compared to baseline methods and ERA5, our model excels in multiple metrics and provides a more precise representation of relative rainfall frequency. Independent verification was performed using station observations during the period of 2010–2015 when CLDAS data were unavailable. During this verification, the PreciDBPN demonstrated exceptional performance and greater robustness compared to the baseline models. Because our method can efficiently downscale precipitation and bias-correct reanalysis data using minimal computational resources, it can be used to generate high-resolution precipitation datasets (0.0625°, hourly) from 1979 to 2022 while correcting for heavy precipitation underestimations in reanalysis data.

Impact of extreme rainfall on high-speed rail (HSR) delays in major lines of China

Abstract Punctuality monitoring and analysis aims to optimize resource allocation for enhancing rail traffic performance and quality. Adverse weather conditions, particularly heavy precipitation events, are recognized as significant drivers of delays and reduced punctuality of the rail system. This study addresses two key research questions using HSR as an example – what is the impact of rainfall on HSR’s delay, and to what extent are HSR vulnerable to rainstorms. The data for the study were collected from the HSR on the major lines of eastern China in the rainy season of 2015-2017 which lasted from May to October. High-resolution precipitation data is integrated with non-spatial HSR operational data using GIS to create composite grids covering buffer zones around HSR lines. These grids match the spatial scale of historical hourly precipitation data and enable regression analyses to assess how precipitation affects HSR operations. The results indicate that extreme rainfall significantly contributes to delays and reduced punctuality, with varying impacts observed across different HSR lines. Specifically, daily areal precipitation significantly delays services on the Hangzhou-Shenzhen and Nanjing-Hangzhou HSR lines. Rainfall intensity has a more pronounced impact on delay services of the Beijing-Shanghai HSR, while extreme precipitation most frequently affects the Shanghai-Nanjing and Jinhua-Wenzhou HSR lines. The case analysis enhances understanding of HSR vulnerability to heavy rainfall conditions and recommends regional adaptation strategies to manage climate-related uncertainties.

Downscaling TRMM Monthly Precipitation in Cloudy and Rainy Regions and Analyzing Spatiotemporal Variations: A Case Study in the Dongting Lake Basin

High-resolution and accurate precipitation data are essential for hydrological, meteorological, and ecological research at the watershed scale. However, in regions with complex terrain and significant rainfall variability, the limited number of rain gauge stations (RGS) is insufficient, and the spatial resolution of existing satellite precipitation data is too low to capture detailed precipitation patterns at the watershed scale. To address this issue, the downscaling of satellite precipitation products has become an effective method to obtain high-resolution precipitation data. This study proposes a monthly downscaling method based on a random forest model, aiming to improve the resolution of precipitation data in cloudy and rainy regions at mid-to-low latitudes. We combined the Google Earth Engine (GEE) platform with a local Python environment, introducing cloud cover characteristics into traditional downscaling variables (latitude, longitude, topography, and vegetation index). The TRMM data were downscaled from 25 km to 1 km, generating high-resolution monthly precipitation data for the Dongting Lake Basin from 2001 to 2019. Furthermore, we analyzed the spatiotemporal variation characteristics of precipitation in the study area. The results show the following: (1) In cloudy and rainy regions, our method improves resolution and detail while maintaining the accuracy of precipitation data; (2) The response of monthly precipitation to environmental variables varies, with cloud cover characteristics contributing more to the downscaling model than vegetation characteristics, helping to overcome the lag effect of vegetation characteristics; and (3) Over the past 20 years, there have been significant seasonal trends in precipitation changes in the study area, with a decreasing trend in winter and spring (January–May) and an increasing trend in summer and autumn (June–December). These results indicate that the proposed method is suitable for downscaling monthly precipitation data in cloudy and rainy regions of the Dongting Lake Basin.

Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations

Abstract. Heavy rainfall is the main driver of soil erosion by water, which is a threat to soil and water resources across the globe. As a consequence of climate change, precipitation – especially extreme precipitation – is increasing in a warmer world, leading to an increase in rainfall erosivity. However, conventional global climate models struggle to represent extreme rain events and cannot provide precipitation data at the high spatiotemporal resolution that is needed for an accurate estimation of future rainfall erosivity. Convection-permitting simulations (CPSs), on the other hand, provide high-resolution precipitation data and a better representation of extreme rain events, but they are mostly limited to relatively small spatial extents and short time periods. Here, we present, for the first time, rainfall erosivity in a large modeling domain such as central Europe based on high-resolution CPS climate data generated with the regional climate model COSMO-CLM using the Representative Concentration Pathway 8.5 (RCP8.5) emission scenario. We calculated rainfall erosivity for the past (1971–2000), present (2001–2019), near future (2031–2060) and far future (2071–2100). Our results showed that future increases in rainfall erosivity in central Europe can be up to 84 % in the region's river basins. These increases are much higher than previously estimated based on regression with mean annual precipitation. We conclude that despite remaining limitations, CPSs have an enormous and currently unexploited potential for climate impact studies on soil erosion. Thus, the soil erosion modeling community should closely follow the recent and future advances in climate modeling to take advantage of new CPSs for climate impact studies.

COMPARISON AND EVALUATION OF MACHINE-LEARNING-BASED SPATIAL DOWNSCALING APPROACHES ON SATELLITE-DERIVED PRECIPITATION DATA

Abstract. Precipitation estimation with high accuracy and resolution is crucial for hydrological and meteorological applications, particularly in ungauged river basins and regions with scarce water resources. Many machine learning (ML) algorithms have been employed in the downscaling of precipitation, however, it remains unclear which algorithm can outperform others. To address this issue, this study evaluates the performance of four ML based downscaling methods to generate high-resolution precipitation estimates at an annual scale. The satellite-derived precipitation data, environmental variables, such as, latitude, longitude, normalized difference vegetation index (NDVI), digital elevation model (DEM), and land surface temperature (LST), as well as the observations from rainfall gauges were used to constructed the regression models. The performance of the four ML algorithms including the Support Vector Regression (SVR), Random Forest (RF), Spatial Random Forest (SRF), and Extreme Gradient Boosting (XGBoost) algorithms was compared with three conventional methods: Multiple Linear Regression (MLR), geographically weighted regression (GWR) and Kriging interpolation model. Results showed that ML-based method generally outperformed traditional interpolation methods in precipitation downscaling, as they had higher accuracy and were better at reproducing the spatial distribution of rainfall. Out of ML approaches, XGBoost received the best performance, followed by SRF, RF and SVR, indicating its robustness of capturing nonlinear relationships. After the XGBoost, better performance of SRF than RF and SVR was found. This might be because the SRF just introduced the spatial autocorrelation into the RF models, which illustrated the importance of capturing spatial variations in ML algorithms. These findings regarding the comparison and assessment provided a novel downscaling method for generating high-resolution precipitation data, which could benefit regional flood forecasting, drought monitoring, and irrigation planning.

The Impact of Extreme Precipitation Events and Their Variability on Climate Change Beliefs in the American Public

Abstract Although scientists agree that climate change is anthropogenic, differing interpretations of evidence in a highly polarized sociopolitical environment impact how individuals perceive climate change. While prior work suggests that individuals experience climate change through local conditions, there is a lack of consensus on how personal experience with extreme precipitation may alter public opinion on climate change. We combine high-resolution precipitation data at the zip-code level with nationally representative public opinion survey results (n = 4008) that examine beliefs in climate change and the perceived cause. Our findings support relationships between well-established value systems (i.e., partisanship, religion) and socioeconomic status with individual opinions of climate change, showing that these values are influential in opinion formation on climate issues. We also show that experiencing characteristics of atypical precipitation (e.g., more variability than normal, increasing or decreasing trends, or highly recurring extreme events) in a local area are associated with increased belief in anthropogenic climate change. This suggests that individuals in communities that experience greater atypical precipitation may be more accepting of messaging and policy strategies directly aimed at addressing climate change challenges. Thus, communication strategies that leverage individual perception of atypical precipitation at the local level may help tap into certain “experiential” processing methods, making climate change feel less distant. These strategies may help reduce polarization and motivate mitigation and adaptation actions. Significance Statement Public acceptance for anthropogenic climate change is hindered by how related issues are presented, diverse value systems, and information-processing biases. Personal experiences with extreme weather may act as a salient cue that impacts individuals’ perceptions of climate change. We couple a large, nationally representative public opinion dataset with station precipitation data at the zip-code level in the United States. Results are nuanced but suggest that anomalous and variable precipitation in a local area may be interpreted as evidence for anthropogenic climate change. So, relating atypical local precipitation conditions to climate change may help tap into individuals’ experiential processing, sidestep polarization, and tailor communications at the local level.

Diurnal Precipitation Features over Complex Terrains along the Yangtze River in China Based on Long-Term TRMM and GPM Radar Products

Based on the 20-year high-resolution precipitation data from TRMM and GPM radar products, diurnal features over complex terrains along the Yangtze River (YR) are investigated. Using the Fast Fourier Transform (FFT) method, the first (diurnal) and second (semi-diurnal) harmonic amplitude and phase of precipitation amount (PA), precipitation frequency (PF), and intensity (PI) are analyzed. The diurnal amplitudes of PA and PF have a decreasing trend from the west to the east with the decreasing altitude of large-scale terrain, while the semi-diurnal amplitudes of PA and PI depict the bimodal precipitation cycle over highlands. For the eastward propagation of PA, PF is capable of depicting the propagation from the upper to the middle reaches of YR, while PI shows the eastward propagation from the middle to the lower reaches of YR during nighttime and presents sensitivity to highlands and lowlands. According to the contribution of different-sized precipitation systems to PI over the highlands and lowlands, the small (<200 km2) ones contribute the least while the large ones (>6000 km2) contribute the most, but the medium ones (200–6000 km2) show a slightly larger contribution over the highlands than over the lowlands. The propagation of each scaled precipitation system along the YR is further analyzed. We found that small precipitation systems mainly happen in the afternoon without obvious propagation. Medium ones peak 2–4 h later than the small ones, with two eastward propagation directions at night from the middle reaches of YR to the east. The large ones are mainly located in lowlands at night, with two propagation routes in the morning over the middle and lower reaches of YR. Such a relay of the propagation of the medium and large precipitation systems explains the eastward movement of PI along the YR, which merits future dynamic studies.

Land market responses to weather shocks: evidence from rural Uganda and Kenya

Abstract This study explores the responses of rural land markets to rainfall shocks in Uganda and Kenya. This study matches the panel data on farm households with rainfall shocks constructed using high-resolution precipitation and temperature data. In both countries, access to credit plays a key role in defining households’ land market responses to rainfall shocks. Households with access to credit respond to rainfall shocks by acquiring more farmland through increased participation in land rental and sales markets. Pathway analyses suggest that exposure to rainfall shock has an impact on land rental prices. There is some evidence that similar to grain reserves and livestock, land markets can provide an avenue for households to respond to rainfall shocks.

Construction of high-resolution precipitation dataset and its implication to drought over the Tianshan Mountains, China

Understanding the drought characteristics of mountainous areas in northwest China with sparse rainfall stations requires high precision, as well as high-resolution precipitation data. Considering the spatial relationship of precipitation and environmental factors, this study downscales Global Precipitation Measurement (GPM) and Multi-Source Weighted-Ensemble Precipitation (MSWEP) based on the geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR) models integrated with interpolation. A high-resolution (1 km×1 km) precipitation dataset during 1979–2020 is reconstructed in the Tianshan Mountains, and the drought characteristics are analyzed by using the optimal dataset. The results show that: 1) Compared with GWR, MGWR model has higher downscaling accuracy; 2) The optimal MSWEP downscaling dataset (CC = 0.93, |BIAS| = 0.48%) compared to GPM (CC = 0.81, |BIAS| = 1.87%) is closer to the observed precipitation; 3) In the past 40 years, 71% and 9% of the Tianshan Mountains show significant wetting and drying trends respectively, and 16 drought events are identified. 4) The West subregion of the Tianshan Mountains is characterized by low frequency, long duration and high severity of drought events. The characteristics of the East are opposite to those of the West. Occasional extreme drought events occur in the North and South. This paper provides data support and method reference for the study of water-vapor balance and regional ecohydrological process in the arid area of Northwest China.

Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin

Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (~10 km–75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of ~88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of ~30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g., zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions.

Change in mean and extreme precipitation in eastern China since 1901

Extreme precipitation in the monsoon region of China can cause a variety of weather and climate disasters, and its long-term change has a significant impact on human life and social production. However, due to the lack of high-resolution precipitation data for the early 20th century, the variation characteristics and causes or mechanisms of extreme precipitation change in eastern China in the last 100 yr or more are still unclear. Based on the ‘daily precipitation dataset of 60 city stations in mainland China from 1901 to 2020’ developed by the National Meteorological Information Center, China Meteorological Administration, the main characteristics of extreme precipitation changes at 38 stations in eastern China during a recent 120 yr period (1901-2020) are analyzed in this paper. The results show that: (1) From 1901 to 2020, there was no significant trend in precipitation amount and precipitation days in the region, but a quasi-period of about 15 to 20 yr or longer existed during the period. (2) The average daily precipitation intensity and most of the extreme precipitation indices first decreased and then increased over the whole time period. In the period 1901-1950, which has rarely been studied in the past, most of the extreme precipitation indices, including intense rainfall, rainstorms, and the maximum precipitation within 1 day, 3 consecutive days, and 5 consecutive days, showed a significant decreasing trend. Since 1951, however, various indices have tended to increase. (3) From 1901 to 2020, the precipitation and extreme precipitation indices to the north of 35° N generally decreased, while they consistently increased in the south. The spatial pattern of extreme precipitation changes for the first half of 20th century was also quite different from that of the last 70 yr. Overall, the mean and extreme precipitation change trends in eastern China were not consistent in different sub-periods during the past century.

Soil Erosion under Future Climate Change Scenarios in a Semi-Arid Region

The Mediterranean Region is presumed to be one of the locations where climate change will have the most effect. This impacts natural resources and increases the extent and severity of natural disasters, in general, and soil water erosion in particular. The focus of this research was to assess how climate change might affect the rate of soil erosion in a watershed in the High Atlas of Morocco. For this purpose, high-resolution precipitation and temperature data (12.5 × 12.5 km) were collected from EURO-CORDEX regional climate model (RCM) simulations for the baseline period, 1976–2005, and future periods, 2030–2060 and 2061–2090. In addition, three maps were created for slopes, land cover, and geology, while the observed erosion process in the catchment was determined following field observations. The erosion potential model (EPM) was then used to assess the impacts of precipitation and temperature variations on the soil erosion rate. Until the end of the 21st century, the results showed a decrease in annual precipitation of −32% and −46% under RCP 4.5 for the periods 2030–2060 and 2061–2090, respectively, −28% and −56% under RCP 8.5 for the same periods, respectively, and a large increase in temperature of +2.8 °C and +4.1 °C for the RCP 4.5 scenario, and +3.1 °C and +5.2 °C for the RCP 8.5 scenario for the periods 2030–2060 and 2061–2090, respectively. The aforementioned changes are anticipated to significantly increase the soil erosion potential rate, by +97.11 m3/km2/year by 2060, and +76.06 m3/km2/year by 2090, under the RCP 4.5 scenario. The RCP 8.5 predicts a rise of +124.64 m3/km2/year for the period 2030–2060, but a drop of −123.82 m3/km2/year for the period 2060–2090.

Revisiting the variations of precipitation and water vapour budget over the Tibetan Plateau

Owing to the scarcity of observation data in the western Tibetan Plateau (TP), the knowledge of precipitation changes over the entire plateau based only on the limited data in eastern TP is not reliable. Therefore, the alternative high-resolution precipitation data of the China Meteorological Forcing Dataset (CMFD) are used for the comprehensive analysis of precipitation changes over the whole TP (including western and northern TP) to fill in the lack of understanding of precipitation in the western TP. Compared with observations, CMFD can broadly capture the spatial distributions and identify the temporal variabilities of precipitation over the TP. Results with CMFD data suggested that the annual precipitation over the whole TP did not show a uniform humidification trend in 1979–2018 and featured wetting and drying trends in the northern (NTP) and southern TP (STP), respectively. Additionally, the four seasonal regimes of precipitation over the northern TP (NTP, including most areas of western TP) all experienced a noticeable interdecadal shift around the late 1990s, followed by above-normal precipitation. Except for spring, the seasonal precipitation over the southern TP (STP) showed interannual variations. Spring precipitation over the STP has undergone moistening since the late 1990s, which was consistent with that over the NTP. Four different reanalysis datasets, namely JRA55, MERRA2, ERA5 and CRA40, were used to compare the water vapour budget of each boundary over the TP. The increase in spring precipitation over the NTP and STP was found to be related to the decrease in water vapour outflow from the north boundary. The interdecadal increase in summer precipitation over the NTP was mainly due to the reduction of outflow from the east boundary. Finally, the increase in autumn precipitation was related to the increase in inflow from the west boundary.

Modelling extremes of spatial aggregates of precipitation using conditional methods

Inference on the extremal behaviour of spatial aggregates of precipitation is important for quantifying river flood risk. There are two classes of previous approach, with one failing to ensure self-consistency in inference across different regions of aggregation and the other imposing highly restrictive assumptions. To overcome these issues, we propose a model for high-resolution precipitation data from which we can simulate realistic fields and explore the behaviour of spatial aggregates. Recent developments have seen spatial extensions of the Heffernan and Tawn (J. R. Stat. Soc. Ser. B. Stat. Methodol. 66 (2004) 497–546) model for conditional multivariate extremes which can handle a wide range of dependence structures. Our contribution is twofold: extensions and improvements of this approach and its model inference for high-dimensional data and a novel framework for deriving aggregates addressing edge effects and subregions without rain. We apply our modelling approach to gridded East Anglia, UK precipitation data. Return-level curves for spatial aggregates over different regions of various sizes are estimated and shown to fit very well to the data.

Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events.

Testing some grouping methods to achieve a low error quantile estimate for high resolution (0.25° x 0.25°) precipitation data

The study focuses on the estimation of a technique, a method for developing a phenomenon, to obtain a quantile with minimal or low error (AR and R-RMSE bias). To arrive at such a solution, a case study of the Mahanadi River system (Mahanadi Basin) was conducted along with the integration of various techniques available in past and present literature, to come up with a novel solution. Which could answer practical questions in water resource planning and management for addressing a wide range of problems such as meteorological draught analysis, agricultural planning, precipitation forecasting and downscaling, design of water control and conveyance structures, and land-use planning and management. A gridded rainfall data set of resolution 0.25° x 0.25° (1901 – 2017) obtained from IMD Pune is used to calculate the statistics that will be used for the regionalization of precipitation. Other attributes or variables used for regionalization are seasonality measurements and location parameters (latitude, longitude, and elevation). The L-moment statistics are computed from the time series rainfall data and the ratios of the L-coefficient of variance and the L-coefficient of skewness, i.e., the L-moment ratio, are the main components in computing quantile estimates of selected regions for effective regional frequency analysis. To determine potential scenarios for homogeneous regions, the use of seasonal extreme precipitation will serve as a basis for regionalization.

Spatial Downscaling Model Combined with the Geographically Weighted Regression and Multifractal Models for Monthly GPM/IMERG Precipitation in Hubei Province, China

High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth’s water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) × 0.1° (longitude), which is too coarse for regional-scale analysis. This study combined the Geographically Weighted Regression (GWR) and the Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG precipitation products from 0.1° × 0.1° (approximately 11 km × 11 km) to 1 km in Hubei Province, China. This work’s results indicate the following: (1) The original GPM product can accurately express the precipitation in the study area, which highly correlates with the site data from 2015 to 2017 (R2 = 0.79) and overall presents the phenomenon of overestimation. (2) The GWR model maintains the precipitation field’s overall accuracy and smoothness, with even improvements in accuracy for specific months. In contrast, the MFRC model causes a slight decrease in the overall accuracy of the precipitation field but performs better in reducing the bias. (3) The GWR-MF combined with the GWR and MFRC model improves the observation accuracy of the downscaling results and reduces the bias value by introducing the MFRC to correct the deviation of GWR. The conclusion and analysis of this paper can provide a meaningful experience for 1 km high-resolution data to support related applications.

Precipitation variability and risk of infectious disease in children under 5 years for 32 countries: a global analysis using Demographic and Health Survey data

Precipitation variability is a potentially important driver of infectious diseases that are leading causes of child morbidity and mortality worldwide. Disentangling the links between precipitation variability and disease risk is crucial in a changing climate. We aimed to investigate the links between precipitation variability and reported symptoms of infectious disease (cough, fever, and diarrhoea) in children younger than 5 years. We used nationally representative survey data collected between 2014 and 2019 from Demographic and Health Survey (DHS) surveys for 32 low-income to middle-income countries in combination with high-resolution precipitation data (via the Climate Hazards Group InfraRed Precipitation with Station dataset). We only included DHS data for which interview dates and GPS coordinates (latitude and longitude) of household clusters were available. We used a regression modelling approach to assess the relationship between different precipitation variability measures and infectious disease symptoms (cough, fever, and diarrhoea), and explored the effect modification of different climate zones and disease susceptibility factors. Our global analysis showed that anomalously wet conditions increase the risk of cough, fever, and diarrhoea symptoms in humid, subtropical regions. These health risks also increased in tropical savanna regions as a result of anomalously dry conditions. Our analysis of susceptibility factors suggests that unimproved sanitation and unsafe drinking water sources are exacerbating these effects, particularly for rural populations and in drought-prone areas in tropical savanna. Weather shifts can affect the survival and transmission of pathogens that are particularly harmful to young children. As our findings show, the health burden of climate-sensitive infectious diseases can be substantial and is likely to fall on populations that are already among the most disadvantaged, including households living in remote rural areas and those lacking access to safe water and sanitation infrastructure. University of California, San Diego FY19 Center Launch programme.

Quality Evaluation of the 0.01° Multi-Source Fusion Precipitation Product and Its Application in Extreme Precipitation Event

High-resolution and high-quality precipitation data play an important role in Numerical Weather Prediction Model testing, mountain flood geological disaster monitoring, hydrological monitoring and prediction and have become an urgent need for the development of modern meteorological business. The 0.01° multi-source fusion precipitation product is the latest precipitation product developed by the National Meteorological Information Center to meet the above needs. Taking the hourly precipitation observation data of 2400 national automatic stations as the evaluation base, independent and non-independent test methods are used to evaluate the 0.01° multi-source fusion precipitation product in 2020. The product quality differences between the 0.01° precipitation product and the 0.05° precipitation product are compared, and their application in extreme precipitation events are analyzed. The results show that, in the independent test, the product quality of the 0.01° precipitation product and the 0.05° precipitation product are basically the same, which is better than that of each single input data source, and the product quality in winter and spring is slightly lower than that in summer, and both products have better quality in the east in China. The evaluation results of the 0.01° precipitation product in the non-independent test are far better than that of the 0.05° product. The root mean square error and the correlation coefficient of the 0.01° multi-source fusion precipitation product are 0.169 mm/h and 0.995, respectively. In the extreme precipitation case analysis, the 0.01° precipitation product, which is more consistent with the station observation values, effectively improves the problem that the extreme value of the 0.05° product is lower than that of station observation values and greatly improves the accuracy of the precipitation extreme value in the product. The 0.01° multi-source fusion precipitation product has better spatial continuity, a more detailed description of precipitation spatial distribution and a more accurate reflection of precipitation extreme values, which will better provide precipitation data support for refined meteorological services, major activity support, disaster prevention and reduction, etc.