Daily Precipitation Characteristics Research Articles

The asymmetric distribution of rainfall frequency and amounts in India

Studies of rainfall usually focus on the total amount precipitating throughout a certain period. Compared to rain rates associated with extreme events, the rain rates associated with the most frequent events is understudied. In this study, the characteristics of daily precipitation in India are explored using two metrics − rain frequency peak (the most frequent non-zero rain rate) and rain amount peak (the rain rate at which the most amount of rain falls). These metrics are computed over India using local and global datasets to investigate the characteristics of typical daily precipitation accumulations. These values are sensitive to the dataset used for this analysis since the temporal and spatial resolution of the rainfall data will influence the rain frequency peak and rain amount peak. Our study reveals the rain frequency peak is highest during the summer monsoon, while the winter season exhibits lower values, particularly at higher latitudes. Similarly, the rain amount distribution indicates dominance of heavy rain rates during the monsoon and post-monsoon seasons, leading to high total precipitation. The maximum rain frequency peak for any region in India reaches up to a value of 35 mm/day while the maximum rain amount peak reaches up to a value as high as 90 mm/day. These metrics would be useful in systematically evaluating typical daily precipitation in regional climate models and assessing downstream impacts of uneven precipitation such as lower crop yields, flood-drought alterations, and fluctuating water availability.

Pattern scaling the parameters of a Markov‐chain gamma‐distribution daily precipitation generator

AbstractGeneral circulation models (GCMs) are the most sophisticated tools at our disposal for studying future climates, but there are limitations to overcome. These include resolutions that may be too coarse for impact assessments, limited or zero availability of some policy‐relevant scenarios, and limited time‐series length for assessing the risk of extreme events. We illustrate how these limitations can be addressed by combining a stochastic precipitation generator (SPG) with pattern scaling (PS) of its key parameters. Computationally inexpensive, SPG parameters can be perturbed to generate time‐series representative of weather under a future climate with high spatial and temporal resolution. If the SPG parameter perturbations are derived directly from GCM simulations projections can only be made for scenarios already simulated by the GCM. Instead, we obtain the parameter perturbations using PS, facilitating emulation of scenarios not necessarily explicitly simulated by the GCM, and where we scale perturbations approximately linearly with global temperature change. PS is commonly applied to estimate perturbations in the mean of climate variables, but rarely to higher‐order parameters as we demonstrate here. We apply PS for the first time, globally, to the parameters of a daily, first‐order Markov‐chain gamma‐distribution SPG using output from the IPSL‐CM6A‐LR GCM to perturb an SPG fitted to observed data from two stations in diverse climates (Santarém, Brazil and Reykjavik, Iceland) to illustrate this novel approach. We produce time series corresponding to a range of GWLs and demonstrate the capability of the combined SPG‐PS approach to study local‐scale, future daily precipitation characteristics, climate and subsequent risk of extreme weather events.

Statistical Evaluation of the Performance of Gridded Daily Precipitation Products from Reanalysis Data, Satellite Estimates, and Merged Analyses over Global Land

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas.

Generalized and finely tunable rainfall prediction based on optimized forecasting model

Agriculture is the most important factor in India for survival. Rainfall is the most critical factor for agriculture. A new approach for designing the network structure in a deep neural network (DNN) based forecasting model for rainfall prediction is presented. The number of atmospheric parameters determines whether the rain will fall. We suggest a systematic approach for analysing various parameters affecting rainfall in the atmosphere in addition to identifying seasonal and diurnal variables, the ground-based weather features Temperature, Relative Humidity, Dew Point, Solar Radiation, Precipitable Water Vapor (PWV) are also identified, and a detailed feature correlation study is presented. Forecasting rainfall in non-monsoon sessions required input values of temperature, wind velocity, humidity, and cloud cover. Among the parameters used to evaluate results were MSE (mean square error), CCI (correlation coefficient), COE (coefficient of efficiency), and MAE (mean absolute error). By using the proposed approach, the proposed scheme establishes that deep learning (DL) can be effective in improving rainfall prediction quality, exploiting the potential of neural network (NN) in using a weather simulator. In terms of daily precipitation characteristics, this various model is applied to 12 different sites under various climatic reinforcements.

Non-uniform changes in different daily precipitation events in the contiguous United States

This study examines changes in characteristics (amount, frequency, intensity) of daily precipitation in the contiguous United States (CONUS) using high-quality records for a long-term period (1900–2018) and a more recent period (1950–2018) at different temporal (annual and seasonal scales) and different spatial scales (national and sub-regional scales). Results show that the patterns of change during the two periods are very similar. First, on the annual basis, we find an overall increase in the total annual precipitation, frequency of wet days, and intensity of precipitation in both periods in the CONUS, with percentages of stations showing significant increasing trends significantly larger than what can be expected by chance. Second, stations with significant increasing trends are mainly concentrated in eastern CONUS, while stations with decreasing trends are located on the west coast and partial southeast coast. Specifically, the amounts and frequencies of light, moderate, and heavy precipitation mostly have significantly increased at more than 10% of stations. In both periods, there is a non-uniform change for three intensity categories of precipitation, with the frequency and total amount of events with higher intensity showing a larger rate of change, resulting in the smaller contribution of light precipitation to annual total precipitation but larger contribution due to heavy precipitation. Such non-uniform changes can also be observed in most sub-regions and seasons. Moreover, the estimated sensitivities of the amount of light, moderate, heavy precipitation, and heaviest precipitation event to global surface temperature increase for the 1900–2018 period is comparable with that for the 1950–2018 period, indicating that sampling period does not have a substantial effect on the scaling relationship between the amount of different precipitation events and global mean temperature.

Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model

Abstract. In this study, we analyze extreme daily precipitation during the pre-industrial period from 1501 BCE to 1849 CE in simulations from the Community Earth System Model version 1.2.2. A peak-over-threshold (POT) extreme value analysis is employed to examine characteristics of extreme precipitation and to identify connections of extreme precipitation with the external forcing and with modes of internal variability. The POT analysis shows that extreme precipitation with similar statistical characteristics, i.e., the probability density distributions, tends to cluster spatially. There are differences in the distribution of extreme precipitation between the Pacific and Atlantic sectors and between the northern high and southern low latitudes. Extreme precipitation during the pre-industrial period is largely influenced by modes of internal variability, such as El Niño–Southern Oscillation (ENSO), the Pacific North American, and Pacific South American patterns, among others, and regional surface temperatures. In general, the modes of variability exhibit a statistically significant connection to extreme precipitation in the vicinity to their regions of action. The exception is ENSO, which shows more widespread influence on extreme precipitation across the Earth. In addition, the regions with which extreme precipitation is more associated, either by a mode of variability or by the regional surface temperature, are distinguished. Regional surface temperatures are associated with extreme precipitation over lands at the extratropical latitudes and over the tropical oceans. In other regions, the influence of modes of variability is still dominant. Effects of the changes in the orbital parameters on extreme precipitation are rather weak compared to those of the modes of internal variability and of the regional surface temperatures. Still, some regions in central Africa, southern Asia, and the tropical Atlantic ocean show statistically significant connections between extreme precipitation and orbital forcing, implying that in these regions, extreme precipitation has increased linearly during the 3351-year pre-industrial period. Tropical volcanic eruptions affect extreme precipitation more clearly in the short term up to a few years, altering both the intensity and frequency of extreme precipitation. However, more apparent changes are found in the frequency than the intensity of extreme precipitation. After eruptions, the return periods of extreme precipitation increase over the extratropical regions and the tropical Pacific, while a decrease is found in other regions. The post-eruption changes in the frequency of extreme precipitation are associated with ENSO, which itself is influenced by tropical eruptions. Overall, the results show that climate simulations are useful to complement the information on pre-industrial extreme precipitation, as they elucidate statistical characteristics and long-term connections of extreme events with natural variability.

Whether the CMIP5 Models Can Reproduce the Long-Range Correlation of Daily Precipitation?

In this study, we investigated the performance of nine CMIP5 models for global daily precipitation by comparing with NCEP data from 1960 to 2005 based on the detrended fluctuation analysis (DFA) method. We found that NCEP daily precipitation exhibits long-range correlation (LRC) characteristics in most regions of the world. The LRC of daily precipitation over the central of North American continent is the strongest in summer, while the LRC of precipitation is the weakest for the equatorial central Pacific Ocean. The zonal average scaling exponents of NCEP daily precipitation are smaller in middle and high latitudes than those in the tropics. The scaling exponents are above 0.9 over the tropical middle and east Pacific Ocean for the year and four seasons. Most CMIP5 models can capture the characteristic that zonal mean scaling exponents of daily precipitation reach the peak in the tropics, and then decrease rapidly with the latitude increasing. The zonal mean scaling exponents simulated by CMCC-CMS, GFDL-ESM2G and IPSL-CM5A-MR show consistencies with those of NCEP, while BCC_CSM1.1(m) and FGOALS-g2 cannot capture the seasonal variations of daily precipitation’s LRC. The biases of scaling exponents between CMIP5 models and NCEP are smaller in the high latitudes, and even less than the absolute value of 0.05 in some regions, including Arctic Ocean, Siberian, Southern Ocean and Antarctic. However, for Western Africa, Eastern Africa, Tropical Eastern Pacific and Northern South America, the simulated biases of scaling exponents are greater than the absolute value of 0.05 for the year and all four seasons. In general, the spatial biases of LRC simulated by GFDL-ESM2G, HadGEM2-AO and INM-CM4 are relatively small, which indicating that the LRC characteristics of daily precipitation are well simulated by these models.

Precipitation Characteristics and Future Changes Over the Southern Slope of Tibetan Plateau Simulated by a High‐Resolution Global Nonhydrostatic Model

AbstractCurrent climate model resolution cannot accurately describe the complex topography over the Tibetan Plateau, which limits our understanding of past and future precipitation over this region. This study investigates the daily precipitation characteristics and its future changes over the Tibetan Plateau, especially for the southern slope of Tibetan Plateau (SSTP), by 14‐km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) with explicitly calculated convection for historical and future 30‐years period. By comparing with the satellite Global Precipitation Measurement (GPM), NICAM well reproduces the historical precipitation spatial pattern, seasonal cycle, and the extreme precipitation belt over SSTP, but overestimates precipitation amount by ∼35%. It is found that heavy precipitation probability decreases as elevation becomes higher while the light precipitation generally shows the opposite. For the precipitation changes during June to September from 1979–2008 to 2075–2104, NICAM predicts that mean precipitation will decrease over low‐level SSTP but increase over high‐level SSTP. Nonprecipitation and heavy precipitation probability will increase while light precipitation probability will decrease in the future over SSTP. The extreme precipitation probability and intensity will increase ∼50%/°C and ∼8%/°C over SSTP, and this increase is more obvious as elevation becomes higher. The robust increase of extreme precipitation along the SSTP topography is unique and has not been identified by the climate model simulations before. The strong meridional gradient of specific humidity over SSTP is found to be further enhanced under global warming, and this gradient enhancement is suggested to be responsible for the increase in the extreme precipitation over SSTP.

The Modulation of Daily Southern Africa Precipitation by El Niño–Southern Oscillation across the Summertime Wet Season

AbstractThe spatiotemporal evolution of daily southern Africa precipitation characteristics, and associated atmospheric circulation, related to El Niño and La Niña is examined across the region’s November–April wet season. Precipitation characteristics are examined in terms of monthly changes in daily average precipitation, the number of precipitation days, and the number of heavy precipitation days in three independently constructed estimates of observed precipitation during 1983–2018. Mechanisms related to precipitation changes, including contributions from mass divergence, water vapor transports, and transient eddies, are diagnosed using the atmospheric moisture budget based on the ERA5 reanalysis. El Niño is related to precipitation anomalies that build during December–March, the core of the rainy season, culminating in significantly below average values stretching across a semiarid region from central Mozambique to southeastern Angola. A broad anticyclone centered over Botswana drives these precipitation anomalies primarily through anomalous mass divergence, with moisture advection and transient eddies playing secondary roles. La Niña is related to significantly above average daily precipitation characteristics over all Africa south of 20°S in February and much less so during the other five months. February precipitation anomalies are primarily driven through mass divergence due to a strong anomalous cyclonic circulation, whereas a similar circulation is more diffuse during the other months. The spatiotemporal evolutions of anomalies in daily precipitation characteristics across southern Africa related to El Niño and La Niña are not equal and opposite. The robustness of an asymmetric evolution, which could have implications for subseasonal forecasts, needs to be confirmed with analysis of additional empirical data and established with climate model experimentation.

Daily precipitation characteristics of RegCM4 and WRF in China and their interannual variations

To evaluate and clarify the daily precipitation characteristics (i.e. amount, frequency and intensity) of the regional climate models (RCMs) in China, long-term simulations were carried out using RegCM4.5 and Weather Research and Forecasting model (WRF), which were nested within the European Centre for Medium-Range Weather Forecasts (ECMWF)’s 20th century reanalysis (ERA-20C) between 1901 and 2010. The 2 RCMs were initially run at a resolution of 50 km. Analyses mainly compared the model-simulated climatic means and interannual variations of precipitation characteristics with those of dense and high-quality station observations (STN) from 1961-2010. Both models satisfactorily reproduced the seasonal mean precipitation amount, but they overestimated its frequency and underestimated its intensity. Extreme rainfall frequency was also underestimated by both RCMs. In winter (DJF), the interannual variabilities in dry days, light precipitation and moderate precipitation were well represented by both models. However, they poorly reproduced the counterparts of extreme precipitation in winter. In summer (JJA), the 2 RCMs performed well in simulating the interannual variability of extreme precipitation. Comparably, RegCM outperformed WRF in reproducing the spatial patterns of precipitation amount, interannual variations in extreme precipitation and rain events. By contrast, WRF better represented precipitation frequency in different sub-regions overall. Moreover, when the horizontal resolution of RegCM was increased from 50 to 25 km, there was a slight improvement in the representation of precipitation amount and intensity. Our results show that RCMs perform well in reproducing actual climatic means and interannual variations of daily precipitation characteristics in China, and that high-resolution RCM simulations can produce improved results for precipitation amount and intensity.

Characteristics of extreme precipitation in South China during April–July for early rice season

Extreme precipitation has significant impacts on human society and agriculture development under global climate change as well as severe effects on rice development. However, little research has been done on the characteristics of extreme precipitation in different rice growth stages. Taking the South China region as a case study, the characteristics of extreme daily precipitation in the early rice season (April–July) from 1960 to 2009 were investigated by using percentile method. Results indicated that extreme precipitation threshold increases since the tillering stage, and there is big difference between the first and the rest growth stages. Extreme precipitation is serious in the eastern part than in the western part of South China during the seeding and tillering stage, and reverse since the booting stage. Frequency of extreme precipitation increases in recent decades after the booting stage. Finally, flood risk regions form more easily in the coastal cities and the western part of South China in the 1990s and 2000s since the tillering stage.

Hybrid Deep Learning Approach for Multi-Step-Ahead Daily Rainfall Prediction Using GCM Simulations

Deep Learning (DL) is an effective technique for dealing with complex systems. This study proposes a hybrid DL approach, a combination of one-dimensional Convolutional Neural Network (Conv1D) and Multi-Layer Perceptron (MLP) (hereinafter referred to as hybrid Conv1D-MLP model), for multi-step-ahead (1-day to 5-day in advance) daily rainfall prediction. Nine meteorological variables, closely associated with daily rainfall variation, are used as inputs to the hybrid model. The causal variables are obtained from a General Circulation Model (GCM). In general, simulation of meteorological variables from GCM is much better than rainfall estimate and observed records of meteorological variables is sparsely available, if not completely unavailable at many locations. Thus, proposed scheme helps to establish the effectiveness of the DL approach in augmenting the quality of rainfall prediction, exploiting the potential of GCM in simulating meteorological variables. The developed hybrid model is applied to twelve different locations in different climatic regimes in terms of daily precipitation characteristics. The proposed hybrid approach is compared with a DL approach namely, Multi-Layered Perceptron (deep MLP) and another machine learning approach namely, Support Vector Regression (SVR). It is also found that the performance of the model gradually decreases as the prediction lead time (in days) increases. Overall, this study establishes the fact that the hybrid Conv1D-MLP model is more effective in capturing the complex relationship between the causal variables and daily variation of rainfall. The benefit is due to the unification of potentials of individual approaches for extracting the hidden features of hydrometeorological association.

An evaluation of COSMO‐CLM regional climate model in simulating precipitation over Central Africa

AbstractIn this study, an analysis of present day climate simulation (1998–2008) is presented for the Central African (CA) region with the COnsortium for Small‐scale MOdelling in CLimate Mode (CCLM) regional climate model, forced by the ERA‐Interim (ERAINT) reanalysis data. The ability of the CCLM to simulate the observed precipitation with particular focus on the mean spatial pattern, low‐level circulation, seasonal cycles, and daily characteristics is evaluated. Likewise, the added value of the regional model CCLM compared to the driving ERAINT reanalysis is also investigated. It is shown that ERAINT and CCLM exhibit quite different sign of bias, which is an indication of the importance of internal variability and fine scale processes representation for the simulation of surface climate. Despite the CCLM is constantly dry over southern CA, the model succeeds to reproduce reasonably the mean spatial patterns of precipitation and low‐level circulation features, along with the associated seasonal cycles over the whole CA and majority of the five selected analysis sub‐regions. Results also show that daily precipitation indices are well represented, although the better performance greatly depends on the considered seasons. Nevertheless, CCLM substantially outperforms the ERAINT daily precipitation characteristics, thus highlighting the added value of the downscaling exercise over the region. The analysis of daily precipitation indices also reveals that the dry character of the model could probably be connected to the underestimation of the simulated less intense events, which in turn result to an overestimation of the simulated dry spell duration.

Stochastic Modeling of Extreme Precipitation: a Regional Approach

A generalized step-by-step statistical method for calculating the maximal precipitation sums of low probability is proposed. The method is applied to the case of daily precipitation for the Amur River basin. Refined statistical characteristics of maximum daily precipitation for the warm period are obtained. A map of daily precipitation of 1% exceedance probability is compiled over the territory of the Amur River. The obtained quantiles of the daily maximum precipitation are compared with probable maximum precipitation values obtained using WMO methodology.

Evaluating the Reliability of the U.S. Cooperative Observer Program Precipitation Observations for Extreme Events Analysis Using the LTAR Network

AbstractThe detection and attribution of changes in precipitation characteristics relies on dense networks of rain gauges. In the United States, the COOP network is widely used for such studies even though there are reported inconsistencies due to changes in instruments and location, inadequate maintenance, dissimilar observation time, and the fact that measurements are made by a group of dedicated volunteers. Alternately, the Long-Term Agroecosystem Research (LTAR) network has been consistently and professionally measuring precipitation since the early 1930s. The purpose of this study is to compare changes in extreme daily precipitation characteristics during the warm season using paired rain gauges from the LTAR and COOP networks. The comparison, done at 12 LTAR sites located across the United States, shows underestimation and overestimation of daily precipitation totals at the COOP sites compared to the reference LTAR observations. However, the magnitude and direction of the differences are not linked to the underlying precipitation climatology of the sites. Precipitation indices that focus on extreme precipitation characteristics match closely between the two networks at most of the sites. Our results show consistency between the COOP and LTAR networks with precipitation extremes. It also indicates that despite the discrepancies at the daily time steps, the extreme precipitation observed by COOP rain gauges can be reliably used to characterize changes in the hydrologic cycle due to natural and human causes.

Daily characteristics of Central African rainfall in the REMO model

In this paper, daily characteristics of the Central Africa rainfall are assessed using the regional model REMO in the framework of contributions to the CORDEX-Africa project. The model is used to dynamically downscale two global climate models (MPI-ESM-LR and EC-EARTH) for the present (1981–2005) and future (2041–2065, 2071–2095) climate under the Representative Concentration Pathway (RCP) 2.6, 4.5, and 8.5 emission scenarios. A substantial spatio-temporal variability of the daily precipitation characteristics is obtained, as well as varying inferences for individual indices. For the present days, both REMO’s runs capture reasonably well the mean seasonal rainfall, the frequency of wet days, the threshold of extreme rainfall, and the cumulative frequency of daily rainfall. The model better simulates the frequency of rainy days than their intensity. It is found that origins of model biases differ as a function of regions. Over the continent, boundary conditions tend to influence the spatial distribution of rainfall whereas over oceanic and coastal regions, REMO’s physics seems to dominate over the boundary forcing. The projected frequency of wet days shows a decrease along the twenty-first century over most part of the continent. Throughout the century, all scenarios of REMO decrease the rate of rainfall with increasing intensity, and which will be noticeable in the Sahelian region at late twenty-first century. Furthermore, the extreme event thresholds decrease over Sahelian regions and increase along the coastal regions.

甘肃中东部地区汛期不同强度日降水特征

甘肃中东部地区汛期不同强度日降水特征

Annual precipitation and daily extreme precipitation distribution: possible trends from 1960 to 2010 in urban areas of China

With global warming, precipitation events are often prone to intensify in some regions. Understanding the changing characteristics of annual and daily extreme precipitation as well as the underlying mechanisms plays an import role for early warning of precipitation-induced disaster (e.g. floods, landslides) and water resources management, especially in densely populated urban areas. In this study, we investigate the long-term trend of annual and daily extreme precipitation in China during 1960–2010 based on daily observations from 539 meteorological stations, and the land cover map with impervious information. We find an overall increasing trend in annual and daily extreme precipitation, particularly in South-East and North-West of China. Moreover, 157 stations located in metropolitan regions experience higher increasing trends of daily extreme precipitation, particularly in Shanghai and Guangzhou metropolitan areas. It is noted that the central urban area of one metropolitan region may have significantly higher increasing trends of daily extreme precipitation than corresponding surrounding areas.

Middle East and Southwest Asia Daily Precipitation Characteristics Associated with the Madden–Julian Oscillation during Boreal Winter

The spatial and temporal evolution of Middle East and southwest Asia (MESW) precipitation characteristics and the associated atmospheric circulation during times in which tropical eastern Indian Ocean precipitation is either enhanced or reduced associated with the Madden–Julian oscillation (MJO) is assessed. Using multiple estimates of both the observed precipitation and the MJO during 1981–2016, the evolution of MESW precipitation characteristics throughout November–April is examined in terms of monthly precipitation accumulation on precipitation days, the number of precipitation days, and the number of extreme precipitation days. MJO phases 2–4, during which eastern Indian Ocean precipitation is enhanced, and MJO phases 6–8, during which eastern Indian Ocean precipitation is reduced, are related, with significant decreases and increases in the number of precipitation days across MESW, respectively. The patterns of precipitation-day changes between MJO phases undergo noteworthy spatial and temporal evolutions across the boreal cold season that are influenced by the interaction between Rossby wave forcing by the MJO and seasonal changes in both the upper-level jet and moisture over the region. During December–January, the changes in precipitation days are found primarily over northern MESW, while during February–March, the changes in precipitation days are found primarily over southern MESW. Although the results identify an important sensitivity in the number of precipitation days over the MESW related to the MJO, the same sensitivity is not apparent in terms of the number of extreme precipitation days and, in particular, the amount of precipitation on a precipitation day.

Stochastic characteristics of daily precipitation and its spatiotemporal difference over China based on information entropy

Daily precipitation process in China showed obvious randomness and spatiotemporal variation. It is important to accurately understand the influence of precipitation changes on control of flood and waterlogging disaster. Using the daily precipitation data measured at 520 stations in China during 1961-2013, we quantified the stochastic characteristics of daily precipitation over China based on the index of information entropy. Results showed that the randomness of daily precipitation in the southeast region were larger than that in the northwest region. Moreover, the spatial distribution of stochastic characteristics of precipitation was different at various grades. Stochastic characteri-stics of P0(precipitation at 0.1-10 mm) was large, but the spatial variation was not obvious. The stochastic characteristics of P10(precipitation at 10-25 mm) and P25(precipitation at 25-50 mm) were the largest and their spatial difference was obvious. P50(precipitation ≥50 mm) had the smallest stochastic characteristics and the most obviously spatial difference. Generally, the entropy values of precipitation obviously increased over the last five decades, indicating more significantly stochastic characteristics of precipitation (especially the obvious increase of heavy precipitation events) in most region over China under the scenarios of global climate change. Given that the spatial distribution and long-term trend of entropy values of daily precipitation could reflect thespatial distribution of stochastic characteristics of precipitation, our results could provide scientific basis for the control of flood and waterlogging disaster, the layout of agricultural planning, and the planning of ecological environment.