Spatial Variability Of Precipitation Research Articles

From rain to data: A review of the creation of monthly and daily station‐based gridded precipitation datasets

AbstractMonthly and daily gridded precipitation datasets are one of the most demanded products in climatology and hydrology. These datasets describe the high spatial and temporal variability of precipitation as a continuous surface and for defined periods. However, due to the complex characteristics of precipitation, it is difficult to obtain accurate estimations. Thus, the creation of a gridded dataset from observations requires the comprehensive and precise application of quality control, reconstruction, and gridding procedures. Yet, despite multiple advances, most of the gridded datasets created and published since the mid‐1990s to the present use a wide variety of techniques, methods, and outputs, which can completely change the final representativity of the data. It is, therefore, critical to provide general guidelines for the development of future and more robust gridded datasets based on the data characteristics, geographical factors, and advanced statistical techniques. We identified gaps and challenges for near‐future perspectives and provide guidelines for implementing improved approaches based on the performance of 48 products. Finally, we concluded that, despite better spatial and temporal resolutions, data access, and data processing capabilities, observational coverage remains a challenge. Moreover, scientists should adopt tailored strategies to improve the representativity and uncertainty of the estimates.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Extremes Science of Water > Methods

Etapas de uma previsão climática sazonal

A previsão climática sazonal é de vital importância para a sociedade, tendo grande relevância para diversos setores como o agrícola, energético, defesa civil, etc. É um processo complexo, que consiste na reunião e interpretação de uma gama muito grande de informações, além da necessidade do conhecimento prévio da climatologia de uma certa região. Este trabalho documenta a sequência de passos que constituem a elaboração de uma previsão climática, utilizando como exemplo a previsão para o Brasil no inverno austral de 2020. As etapas de uma previsão (determinação de climatologias, análise diagnóstica, prognóstico sazonal e validação subjetiva) são apresentadas e desenvolvidas, e os prognósticos incorretos de anomalias de precipitação no sul e no norte do país são analisados em termos de possíveis forçantes climáticas que levaram à sua ocorrência. São enumerados, por fim, os desafios inerentes à previsão climática, como a atuação conjunta e a relevância relativa de diferentes oscilações climáticas, a grande variabilidade espacial da precipitação e o cuidado com as previsões indicadas pelos modelos numéricos.

Estimation of Terrestrial Water Storage Changes at Small Basin Scales Based on Multi-Source Data

Terrestrial water storage changes (TWSCs) retrieved from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have been extensively evaluated in previous studies over large basin scales. However, monitoring the TWSC at small basin scales is still poorly understood. This study presented a new method for calculating TWSCs at the small basin scales based on the water balance equation, using hydrometeorological and multi-source data. First, the basin was divided into several sub-basins through the slope runoff simulation algorithm. Secondly, we simulated the evapotranspiration (ET) and outbound runoff of each sub-basin using the PML_V2 and SWAT. Lastly, through the water balance equation, the TWSC of each sub-basin was obtained. Based on the estimated results, we analyzed the temporal and spatial variations in precipitation, ET, outbound runoff, and TWSC in the Ganjiang River Basin (GRB) from 2002 to 2018. The results showed that by comparing with GRACE products, in situ groundwater levels data, and soil moisture storage, the TWSC calculated by this study is in good agreement with these three data. During the study period, the spatial and temporal variations in precipitation and runoff in the GRB were similar, with a minimum in 2011 and maximum in 2016. The annual ET changed gently, while the TWSC fluctuated greatly. The findings of this study could provide some new information for improving the estimate of the TWSC at small basin scales.

Spatial and temporal variability in extreme precipitation in the Pearl River Basin, China from 1960 to 2018

AbstractExtreme precipitation events are an increasingly important issue, and their changing trends vary by geographical region. In this study, 12 extreme precipitation indices (EPI) were selected to analyse the temporal and spatial distribution characteristics of extreme precipitation, explore the relationships between EPI and annual total precipitation (ATP) and elevation in the Pearl River Basin from 1960 to 2018, and investigate the persistence of extreme precipitation in the future. The results showed that the regionally averaged ATP insignificantly decreased at a rate of 27.27 mm/decade, while the maximum 1‐day precipitation and maximum 5‐day precipitation exhibited insignificantly increased trends, simple daily intensity index significantly increased at a rate of 0.18 mm/day per decade, whereas wet days, heavy precipitation days and consecutive dry days significantly decreased from 1960 to 2018. Trends in the EPI in four seasons showed large differences, most of the EPI in spring were decreased, while increased in winter. Most EPI in the western regions showed decreasing trends, whereas in the southeastern regions exhibited positive trends. The large spatial variability of EPI is mainly due to the impacts of terrain and water vapour sources. The variations in the extreme precipitation are closely related to the atmospheric circulation index, especially for the East Asian Summer Monsoon. All indices except consecutive dry days exhibited significant positive correlations with ATP. All indices except consecutive dry days had negative correlations with elevation, and extreme precipitation events mainly occurred in lower elevation regions. All the EPI had long‐range correlations based on R/S analysis (H > 0.5), thereby indicating that the EPI will maintain their current trends in the future. This research will provide support for water resource management and scientific adaptation, mitigation and response to climate anomalies.

Characteristics of water requirement of table grape in the growing season

To quantitatively assess the satisfaction degree of precipitation on water requirement of table grape in the main producing areas in China, we analyzed the temporal and spatial variations of precipitation, water requirement and water deficit in different growth stages of table grape based on the 1981-2016 daily meteorological data from 429 meteorological stations in the study region (Jinlin and Liaoning of Northeast China; Shanxi and Hebei of North China; Xinjiang, Gansu, Ningxia and Shaanxi of Northwest China; Sichuan, Guizhou and Yunnan of Southwest China; Jiangsu, Shandong, Zhejiang, Anhui, Fujian, Henan, Hubei, Hunan and Guangxi of Southeast China). Results showed that precipitation in each growth stage showed an increasing trend from north to south and from west to east in the study period. The precipitation in germination-flowering stage was the lowest and showed a decreasing trend. The precipitation in maturation-defoliation stage showed a decreasing trend, while that in flowering-veraison and veraison-maturation stage exhibited an increasing trend. Water requirement of grape exhibited an increasing trend in each growth stage in the study region. Water requirement of grape in Xinjiang and the northern of Gansu Province was the highest. The precipitation could not meet water requirement of grape in Xinjiang, northern Gansu, Ningxia, northern Shaanxi, northern Shanxi, northern Hebei, western Liaoning and western Jilin in each growth stage, as well as northern Yunnan and southern Sichuan during germination-flowering stage. In constrast, water surplus was obvious in the other areas, especially in the southeast and southwest of China. The water deficit of grape showed an increasing trend during the germination-flowering and maturation-defoliation stage, while that during flowering-veraison and veraison-maturation stage showed a decreasing trend.

Evaluation of GPM IMERG Products and Estimation of Warm-Season Precipitation in Crimea

Purpose. The study was aimed at the evaluation of the Integrated MultisatellitE Retrievals from GPM (IMERG) remote sensing dataset using ground observations and estimation of the 2006–2018 warm-season precipitation in the Crimean Peninsula. Methods and Results. Evaluation of the IMERG dataset was performed using the meteorological station observations treated as the ground truth. We provided the multiyear statistical characteristics of precipitation amounts, frequency and intensity for different climate zones of the Crimean Peninsula. We considered the spatial variability of summer precipitation, bias and correlation between IMERG and the ground observations. Conclusions. IMERG has a weaker spatial variability compared to the ground observations. The warm-season IMERG bias is small in the central and mountainous parts of Crimea, whereas the precipitation estimates in the coastal zones are substantially overestimated. The IMERG wet bias is mostly caused by the excessive rainfall frequency. The temporal variability of IMERG is in good agreement with the observations with an average correlation coefficient of 0.73. For most of the metrics considered, warm-season IMERG precipitation significantly outperforms the other datasets in the central and mountainous parts of Crimea and could be used for practical tasks with certain precautions. At the same time, due to the lack of calibration over the marine areas, the quality of IMERG precipitation estimates in the coastal zones is reduced.

Verifikatsiya dannykh distantsionnogo zondirovaniya GPM IMERG i kolichestvennyye otsenki atmosfernykh osadkov v Krymskom regione v teploye vremya goda

Purpose. The study was aimed at the evaluation of the Integrated MultisatellitE Retrievals from GPM (IMERG) remote sensing dataset using ground observations and estimation of the 2006–2018 warmseason precipitation in the Crimean Peninsula. Methods and Results. Evaluation of the IMERG dataset was performed using the meteorological station observations treated as the ground truth. We provided the multiyear statistical characteristics of precipitation amounts, frequency and intensity for different climate zones of the Crimean Peninsula. We considered the spatial variability of summer precipitation, bias and correlation between IMERG and the ground observations. Conclusions. IMERG has a weaker spatial variability compared to the ground observations. The warm-season IMERG bias is small in the central and mountainous parts of Crimea, whereas the precipitation estimates in the coastal zones are substantially overestimated. The IMERG wet bias is mostly caused by the excessive rainfall frequency. The temporal variability of IMERG is in good agreement with the observations with an average correlation coefficient 0.73. For most of the metrics considered, warm-season IMERG precipitation significantly outperforms the other datasets in the central and mountainous parts of Crimea and could be used for practical tasks with certain precautions. At the same time, due to the lack of calibration over the marine areas, the quality of IMERG precipitation estimates in the coastal zones is reduced.

Spatial Variability of Water Resources State of Regions around the “Belt and Road”

Water resource has become a key constraint for implementing the “Belt and Road” initiative which was raised by the Chinese government. Besides the study of spatial and temporal variability of precipitation, this study created a water hazard risk map along the “Belt and Road” zone through combined flood and drought data from 1985. Our results showed that South-Eastern Asia, southern China and eastern Southern Asia are areas with the most abundant precipitations, while floods in these areas are also the most serious. Northwest China, Western Asia, Northern Africa and Southern Asia are areas highly vulnerable to drought. Furthermore, the potential influence of flood and drought were also analyzed by associating with population distribution and corridor map. It reveals that China, South-Eastern Asia, Southern Asia, Western Asia and Northern Africa have the largest population number facing potential high water hazard risk. China–India–Burma Corridor and China–Indo-China Peninsula Corridor have the largest areas facing potential high water hazard risk.

Offline Single-Polarization Radar Quantitative Precipitation Estimation Based on a Spatiotemporal Deep Fusion Model

Quantitative precipitation estimation (QPE) based on Doppler radar plays an important role in severe weather monitoring, industrial and agricultural production, and natural disaster prediction and prevention. However, the temporal and spatial variability of precipitation leads to large errors in radar estimates of mixed precipitation. To improve the accuracy of radar QPE, we propose an offline spatiotemporal deep fusion model that uses the reflectivity data of the Shijiazhuang Doppler radar Z9311 and the precipitation data from 17 national weather stations (NWSs) and 260 automatic weather stations (AWSs). Considering the abrupt spatial changes in precipitation, a three-dimensional radar data structure is proposed, and the spatial features of multielevation and multiscale radar data are extracted and merged using the feature fusion network (FFNet). Finally, the time dependence of the precipitation is captured using the long short-term memory (LSTM) network, and the precipitation estimation is obtained. Based on a comparison of the results of the proposed model (FFNet-LSTM) with those of the ordinary kriging (OK) interpolation, two Z-R relationship, the multilayer perceptron (MLP), the LSTM, and the FFNet, the proposed method is superior to these models, has a promising performance, and is a general-purpose rainfall algorithm.

The role of storm movement in controlling flash flood response: an analysis of the 28 September 2012 extreme event in Murcia, southeastern Spain

AbstractFlash flooding is strongly modulated by the spatial and temporal variability in heavy precipitation. Storm motion prompts a continuous change of rainfall space-time variability that interacts with the drainage river system, thus influencing the flood response. The impact of storm motion on hydrological response is assessed for the 28 September 2012 flash flood over the semi-arid and medium-sized Guadalentín catchment in Murcia, southeastern Spain. The influence of storm kinematics on flood response is examined through the concept of ‘catchment-scale storm velocity’. This variable quantifies the interaction between the storm system motion and the river drainage network, assessing its influence on the hydrograph peak. By comparing two hydrological simulations forced by rainfall scenarios of distinct spatial and temporal variability, the role of storm system movement on the flood response is effectively isolated. This case study is the first to: (i) show through the catchment-scale storm velocity how storm motion may strongly affect flood peak and timing; and (ii) assess the influence of storm kinematics on hydrological response at different basin scales. In the end, this extreme flash flooding provides a valuable case study of how the interaction between storm motion and drainage properties modulate hydrological response.

Temporal and Spatial Variation Characteristics of Precipitation in the Haihe River Basin under the Influence of Climate Change

The impact of global climate change on the temporal and spatial variations of precipitation is significant. In this study, daily temperature and precipitation data from 258 meteorological stations in the Haihe River Basin, for the period 1960–2020, were used to determine the trend and significance of temperature and precipitation changes at interannual and interseasonal scales. The Mann–Kendall test and Spearman’s correlation analysis were employed, and significant change trends and correlations were determined. At more than 90% of the selected stations, the results showed a significant increase in temperature, at both interannual and interseasonal scales, and the increasing trend was more significant in spring than in other seasons. Precipitation predominantly showed a decreasing trend at an interannual scale; however, the change trend was not significant. In terms of the interseasonal scale, the precipitation changes in spring and autumn showed an overall increasing trend, those in summer showed a 1:1 distribution ratio of increasing and decreasing trends, and those in winter showed an overall decreasing trend. Furthermore, the Spearman’s correlation analysis showed a negative correlation between temperature and precipitation in the entire Haihe River Basin, at both interannual and interseasonal scales; however, most of the correlations were weak.

Recharge área maps from precipitation isoscapes. Case study: Aburrá Valley, Colombia

The environmental isotopes are a tool for interpreting the hydrological processes related to underground environments. These are used for the validation of hydrogeological models, in terms of recharge, transit and discharge zones, as well as the identification of groundwater interactions with other water bodies and groundwater residence time in the hydrogeological units. The determination of the recharge processes have usually been validated from knowledge about the stable isotopes present in the water molecule (18O and 2H). At the regional level, this evaluation has been addressed through the construction of isoscapes combined with an understanding of the hydrological characteristics present in the study area and knowledge of conceptual hydrogeological models. The objective of this study is to apply a scheme for the analysis of potential groundwater recharge zones, from 18O and 2H spatial variability in precipitation, baseflow and groundwater, using spatial modelling tools. Based on a preliminary work done for the Antioquia department, a strategic area was chosen in which the results are more forceful and have greater implications in groundwater management: the Aburrá Valley-Colombia.

Characterizing Uncertainties in Ground “Truth” of Precipitation Over Complex Terrain Through High‐Resolution Numerical Modeling

AbstractGround observation of precipitation over complex terrain is subject to large uncertainties due to inadequate sampling. This study explores a method that combines limited gauge data and a high‐resolution numerical simulation to quantify the precipitation uncertainties in central Himalaya. Specifically, the Coefficient of spatial Variability (CV) of precipitation and the minimum Number of Required Stations (NRS) to obtain areal‐mean precipitation ground truth values within a 0.25° area are investigated using fine‐scale meteorological simulation at 1.5 km grid spacing. Evaluation over a densely‐gauged area demonstrates comparable CV and NRS values between station observations and simulations. The simulated CV and NRS values in a larger area show a strong and positive dependence on each other and an expected positive (negative) correlation with topographic complexity (temporal scale). The proposed method sheds lights on evaluating precipitation products and holds promise for informing the layout of rain gauge networks in complex terrain.

Interactions between species richness, herbivory and precipitation affect standing biomass in Mongolian rangelands

AbstractQuestionsLivestock management in rangelands depends on the production of plant biomass. Biomass production is driven by the temporal and spatial variability in precipitation, but our understanding of how precipitation variability mediates grazing effects on biomass production is still fragmented. Along a 600‐km precipitation gradient we extracted biomass data to ask the questions: (a) what are the effects of grazing intensity on biomass production; (b) does grazing intensity interact with plant species richness to affect biomass production; and (c) how do plant functional groups respond to grazing and precipitation?LocationMongolia.MethodsBiomass was sampled along 15 grazing intensity transects within the precipitation gradient over two consecutive years. We modeled spatial variability in above‐ground plant biomass using mixed‐effects models. Normalized difference vegetation index (NDVI) data were combined with field‐sampled biomass data to correct for inter‐annual precipitation variation. The effects of species richness were modeled with respect to possible interactions with grazing intensity, and the composition of plant functional groups was modeled with respect to possible interactions between grazing intensity and precipitation.ResultsBiomass was negatively correlated with grazing intensity and this effect increased as precipitation increased. Biomass was positively correlated with species richness in both years, but the strength of this effect and the interaction between species richness and grazing intensity differed between 2014 and 2015 in line with highly variable precipitation between both years. The plant functional groups grasses, sedges, legumes, wormwood and forbs had contrasting responses to grazing and precipitation.ConclusionBiomass production in drylands is more vulnerable to changes in precipitation variability and grazing intensities in relatively moist and productive rangelands than in dry and unproductive ones. Future rangeland management needs to address potentially increasing precipitation variability in order to promote desired forage plants, and to preserve the positive effects of biodiversity for biomass production.

An EnKF-based method to produce rainfall maps from simulated satellite-to-ground MW-link signal attenuation.

Measuring rainfall is complex, due to the high temporal and spatial variability of precipitation, especially in a changing climate, but it is of great importance for all the scientific and operational disciplines dealing with rainfall effects on the environment, human activities, and economy. Microwave (MW) telecommunication links carry information on rainfall rates along their path, through signal attenuation caused by raindrops, and can become measurements of opportunity, offering inexpensive chances to augment information without deploying additional infrastructures, at the cost of some smart processing. Processing satellite telecom signals bring some specific complexities related to the effects of rainfall boundaries, melting layer, and non-weather attenuations, but with the potential to provide worldwide precipitation data with high temporal and spatial samplings. These measurements have to be processed according to the probabilistic nature of the information they carry. An EnKF-based (Ensemble Kalman Filter) method has been developed to dynamically retrieve rainfall fields in gridded domains, which manages such probabilistic information and exploits the high sampling rate of measurements. The paper presents the EnKF method with some representative tests from synthetic 3D experiments. Ancillary data are assumed as from worldwide-available operational meteorological satellites and models, for advection, initial and boundary conditions, rain height. The method reproduces rainfall structures and quantities in a correct way, and also manages possible link outages. It results computationally viable also for operational implementation and applicable to different link observation geometries and characteristics.

A Modeling Study of the Tripole Pattern of East China Precipitation Over the Past 425 ka

AbstractThe traditional paleoclimatological view suggests that monsoonal China was overall wetter when the East Asian summer monsoon (EASM) was strong. However, this view is at odds with the understanding of modern climate, which demonstrates an analogous “tripole precipitation pattern” in monsoonal China. Little is known about the spatial pattern of paleo‐precipitation in monsoonal China, particularly during the past glacial‐interglacial cycles. Here, we provide evidence for a potential tripole precipitation pattern in monsoonal China over the past 425 ka using climate modeling, and compare the results with paleoclimate records available from China. Our simulations illustrate that more (less) precipitation in North China and South China, and less (more) precipitation in the Central‐East China during strong (weak) EASM periods associated with high (low) boreal summer insolation. Our results agree with the present understanding of modern East Asian climate, and furthermore confirm that the boreal summer insolation is the dominant forcing for the intensity of EASM and the response of subtropical high pressures is fundamental in modulating the precipitation pattern in monsoonal China on orbital timescales. This temporal and spatial variability of precipitation, as revealed in our simulations, shows the potential high complexities in hydroclimatic conditions in monsoonal China throughout the past glacial‐interglacial cycles. Nevertheless, whether the tripole precipitation pattern in monsoonal China over the past 425 ka is robust enough still should be tested, preferable by taking advantage of existing and new well‐dated and explicit indicative precipitation archives in Central‐East China.

A downscaling approach for constructing high-resolution precipitation dataset over the Tibetan Plateau from ERA5 reanalysis

Current gridded precipitation datasets are hard to meet the requirements of hydrological and meteorological applications in complex-terrain areas due to their coarse spatial resolution and large uncertainties. High-resolution atmospheric simulations are capable of describing the influence of topography on precipitation but are difficult to be used to obtain long-term precipitation datasets because they are computationally expensive, while reanalysis data has a long-term coverage and can provide reasonable large-scale spatial and temporal variability of precipitation. This study presents an approach to obtain long-term high-resolution precipitation datasets over complex-terrain areas by combining the ERA5 reanalysis with short-term high-resolution atmospheric simulation. The approach consists of two main steps: first, the ERA5 precipitation is corrected by the high-resolution simulation at the coarse spatial resolution; second, the corrected data is downscaled using a convolution neural network (CNN) based model at daily scale. The proposed approach is applied to the Tibetan Plateau (TP). The downscaled results from ERA5 have a finer spatial structure than ERA5 and can reproduce the spatial patterns of precipitation revealed by the high-resolution simulation. An evaluation based on rain gauge data shows that the downscaled ERA5 has remarkably lower biases than the original ERA5 which overestimates precipitation a lot, and even higher accuracy than the high-resolution simulation data over the TP. The downscaled ERA5 preserves the temporal characteristics of ERA5 which are more consistent with the rain gauge data than that of high-resolution simulation. Since this approach is much less computing resources consuming than the high-resolution simulation, it is an effective method to obtain long-term high-resolution precipitation datasets in complex-terrain areas and is expected to have extensive applications.

Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme

The retrieval of precipitation (snowfall and rainfall) from satellite sensors on a global basis is essential in aiding our knowledge and understanding of the Earth System and for many societal applications. Measurements from surface-based instruments are essentially limited to populated regions, necessitating the use of satellite-based observations to provide estimates of precipitation across the whole of the Earth’s surface. The temporal and spatial variability of precipitation requires adequate sampling, especially at finer resolutions. It is, therefore, necessary to exploit all available data from precipitation-capable satellites to ensure the proper representation of precipitation. To date, the estimation of precipitation using passive microwave observations has been largely concentrated upon the conically scanning imaging instruments, with relatively few techniques exploiting the observations made from the cross-track sounders. This paper describes the development of the Precipitation Retrieval and Profiling Scheme (PRPS) to retrieve precipitation from cross-track sensors, together with its performance against surface radar data and other satellite precipitation retrievals.

Temporal and spatial distributions of precipitation on the Huang-Huai-Hai Plain during 1960–2019, China

AbstractThe Huang-Huai-Hai Plain is an important commercial grain production base in China. Understanding the temporal and spatial variations in precipitation can help prevent drought and flood disasters and ensure food security. Based on the precipitation data for the Huang-Huai-Hai Plain from 1960 to 2019, this study analysed the spatiotemporal distribution of total precipitation at different time scales using the Mann–Kendall test, the wavelet analysis, the empirical orthogonal function (EOF), and the centre-of-gravity model. The results were as follows: (1) The winter precipitation showed a significant upward trend on the Huang-Huai-Hai Plain, while other seasonal trends were not significant. (2) The precipitation on the Huang-Huai-Hai Plain shows a zonal decreasing distribution from southeast to northwest. (3) The application of the EOF method revealed the temporal and spatial distribution characteristics of the precipitation field. The cumulative variance contribution rate of the first two eigenvectors reached 51.5%, revealing two typical distribution fields, namely a ‘global pattern’ and a ‘north-south pattern’. The ‘global pattern’ is the decisive mode, indicating that precipitation on the Huang-Huai-Hai Plain is affected by large-scale weather systems. (4) The annual precipitation barycentres on the Huang-Huai-Hai Plain were located in Jining city and Taian city, Shandong Province, and the spatial distribution pattern was north-south. The annual precipitation barycentres tended to move southwest, but the trend was not obvious. The annual precipitation barycentre is expected to continue to shift to the north in 2020.

High-resolution bias-corrected precipitation data over South Siberia, Russia

The accuracy of global hydrometeorological data is important for regional and global climate studies. We compared the ERA5 reanalysis monthly precipitation data of European Centre for Medium-range Weather Forecasts over South Siberia against observed precipitation data records from 132 ground weather stations for 1979–2015. The ERA5 reanalysis provides detailed fields reasonably reproducing features of mesoscale precipitation structures related to topography and synoptic-scale patterns. The linear correlation coefficients between reanalysis and weather station data are high but mean values are biased. The mean absolute error varies from −23 mm to 90 mm. A bias correction procedure was suggested to improve the precipitation data quality and reduce mean values error. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Raw ERA5 data were scaled according to the derived scale coefficient, resulting in monthly maps of corrected reanalysis for South Siberia (CRSS). Multiply validation of bias correction was performed on the control test sets. The CRSS data fit the observation data very well. The mean error does not exceed 7 mm, the mean absolute error maximal value is 44 mm, and the mean relative error is less than 23%. The maximal biases are typical for mountain areas. The statistical downscaling methods applied in this study can be easily transferred to other regions where long-term data sets of observed precipitation are available. The obtained results are promising since the proposed simple correction scheme is more accurate and robust in reproducing monthly - precipitation values than global reanalysis data. CRSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CRSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results.