Daily Precipitation Dataset Research Articles

Estimating daily precipitation climatology by postprocessing high‐resolution reanalysis data

AbstractSpatial information of climatological frequency distribution of daily precipitation is highly valuable for a wide range of applications. Accurate estimation of climatology can be made for gauged locations where quality and lengthy observations are available. For ungauged or poorly gauged locations, however, indirect estimation is needed. One approach is to use a gridded daily precipitation dataset derived from interpolating observations. However, gridded daily precipitation data can be subject to large errors when gauge density is low. In addition, most interpolation methods tend to smooth the extreme values and increase the low ones, leading to unrealistic statistical properties and therefore poor estimation of daily climatology. Another approach is to first derive climatology at gauged locations and then interpolate climatology to ungauged locations. While this approach is likely to be more robust than the first approach, low gauge density can still cause significant errors especially in areas of complex terrain. In this study, we develop a method that postprocesses spatially consistent and rich reanalysis data using accurate observations at gauged locations. At an ungauged location, daily precipitation amounts from the reanalysis are bias‐corrected using quantile‐mapping guided by frequency distributions of reanalysis data and observations at a nearby gauged location (reference location). The bias‐corrected precipitation amounts are then used to estimate the climatology for the ungauged location. This method eliminates the need for interpolation and therefore its adverse effects. Special care is taken in quantile‐mapping when extrapolating beyond the range of reanalysis data at the reference location. We evaluate the method at 50 locations in Australia, using the Bureau of Meteorology Atmospheric high‐resolution Regional Reanalysis for Australia (BARRA) and precipitation observation network across Australia. These locations are chosen to represent different climate regions in Australia and have observations to validate the postprocessed reanalysis climatology of daily precipitation. Results show that the postprocessed climatology is consistent with observations, in terms of frequency distribution, high quantiles, probabilities of wet and dry days and their transitions.

Historical assessment and future projection of extreme precipitation in CMIP6 models: Global and continental

AbstractIn this study, the new gridded daily precipitation dataset REGEN (Rainfall Estimates on a Gridded Network) was used to verify the performance of 23 global climate models participating in the 6th phase of the Coupled Model Intercomparison Project (CMIP6) in terms of extreme precipitation over global and continents during 1950–2014. Projected changes were also evaluated using CMIP6 multi‐model ensemble (MME) under different Shared Socioeconomic Pathway (SSP) by the end of the 21st century. Although positive biases exist in the climatology of most extreme precipitation indices simulated by the CMIP6 MME compared with observations, most of the CMIP6 models can reproduce the spatial distribution characteristics and temporal evolution trends of global extreme precipitation. The simulation is improved by using the CMIP6 MME, relative to the results from individual models. The projected results showed that in most regions of the world, extreme precipitation will significantly increase in the future, with more significant increase under higher SSPs. By the end of the 21st century, total precipitation of wet days (PRCPTOT), simple daily intensity (SDII), the number of very heavy rain days (R20mm), maximum 1‐day precipitation (Rx1day) and maximum consecutive 5‐day precipitation (Rx5day) over the global will increase by up to 50%, 20%, 70%, 50% and 50%, respectively, under the SSP5‐8.5 scenario. Nevertheless, marked uncertainties exist in the projection of extreme precipitation in South America and in the global for consecutive dry days (CDD) index. Noteworthy, average PRCPTOT, Rx1day and Rx5day over Asia, Europe and North America, as well as of the whole world, have significantly increased where the CDD index is also higher, shown by projected results. In summary, global extreme precipitation will increase in the future, and precipitation will become more uneven in space and time. Moreover, as the SSP scenario becomes higher, the uneven distribution of precipitation will become more pronounced.

Seasonal dynamics of canopy interception loss within a deciduous and a coniferous forest

AbstractCanopy interception loss is a key process in forest hydrology and the role of interception loss in relation to the forest water budgets and future impact of afforestation on water resources is important to quantify. Based on high frequency in situ monitoring, the effect of species‐ and leaf‐cover‐specific canopy structure metrics for interception loss estimation is examined at the two most typical forest types in Denmark. First, interception loss is estimated from precipitation and throughfall data collected in two even‐aged (40–60 years) temperate oak (deciduous) and Norway spruce (coniferous) forests over 13 and 11 months, respectively. Second, based on these observations we estimated canopy structure parameters relevant for interception loss; direct throughfall (ρ), the precipitation necessary to saturate the canopy (P'), the canopy evaporation as a fraction of precipitation (EC/P) and canopy storage capacity (S). Third, we compare observation‐based interception loss with predictions obtained by the analytical Gash interception model using the derived canopy structure parameters. Lastly, the effect of species‐ and leaf‐cover on the interception loss is quantified by applying the canopy structure parameters of the deciduous and coniferous forest on the same daily precipitation data set of approximately 1 year in the Gash model. We found that the derived canopy structure parameters reflect the seasonal change in the leaf cover of the deciduous forest and different parameters are identified for the two forest types. In the deciduous forest, improved agreement between observation‐based and predicted interception loss is obtained using canopy structure parameters for the leafless and full‐foliage periods instead of annual average values. The share of throughfall and interception loss to precipitation (total sum of precipitation = 526 mm) is 65% (340 mm) and 35% (186 mm) for the deciduous forest, respectively, and 49% (260 mm) and 51% (266 mm) for the coniferous forest, respectively. A seasonal variation of interception loss is observed in the deciduous forest where the share of interception loss to precipitation is 40% during the period with leaves on the trees (June to November) and 22% for the leafless periods (December to May). Thus, species‐ and leaf‐cover‐specific canopy structure metrics enhance model performance of throughfall and interception loss dynamics in forests.

Changes in extreme precipitation over Mpologoma catchment in Uganda, East Africa

Changes of five Extreme Precipitation Indices (EPIs) over Mpologoma catchment were examined. The EPIs were obtained from the long-term (1948–2016) gridded (0.25° × 0.25°) Princeton Global Forcing (PGF) daily precipitation dataset. The number of days with precipitation intensity greater than 5 mm/day (NWD5) and 10 mm/day (NWD10) were part of the five EPIs. Trend and variability analyses were by the nonparametric technique based on the cumulative sum of the difference between exceedance and non-exceedance counts of data. The simple multiplicative bias correction method was employed to bias correct the PGF dataset using observed rainfall from nine weather stations. The number of wet days were decreasing although insignificant (p > 0.05). On the other hand, the annual total precipitation considering days with rainfall intensities greater than 5 mm/day (SPre5) exhibited a significant (p < 0.05) decrease. However, annual maxima rainfall (ANMS) increased even though insignificant. This indicates that less frequent rains are received over the study area but some events having high intensity. The insights from the findings of this study constitute a good baseline for predictive planning of water resources management applications across Mpologoma catchment.

Applicability comparison of various precipitation products of long-term hydrological simulations and their impact on parameter sensitivity

Precipitation is an important component of water circulation and an essential input for various hydrological models. A high quality, high spatial resolution, and long-term precipitation dataset would benefit hydrological investigations, particularly for regions having insufficient precipitation records. The upper Huaihe River Basin (UHRB) was selected as the research location in this study, and the accuracies of three precipitation products (PPs: a high-resolution daily gridded precipitation dataset for China (HRLT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Global (CPC) precipitation dataset) were assessed at multiple spatio-temporal scales comparing with the gauge precipitation (GP) for 2000–2019. Subsequently, the applicability of the three PPs on streamflow (Q) and sediment yield (SY) simulations, as well as the impact on parameter sensitivity, were compared using the Soil and Water Assessment Tool (SWAT) model. The results showed that the accuracy of the three PPs were ranked as CPC > HRLT > PERSIANN-CDR on the watershed average scale, HRLT would underestimate the extreme precipitation; and PERSIANN-CDR would overestimate the annual precipitation. On the grid-to-point scale, PERSIANN-CDR was found to be the most stable with high accuracy, followed by CPC and HRLT on all temporal scales. The ability of these PPs to detect rainfall events was ranked as CPC > HRLT > PERSIANN-CDR. The sensitivity of the Q parameters changed with the variation in the precipitation input. The sensitive parameters for GP were distributed on average for almost all processes, while the sensitive parameters for PPs mainly controlled the groundwater and evapotranspiration processes. Among all the PPs, the performance of CPC in the Q and SY simulations was found to be the best, followed by HRLT and PERSIANN-CDR, and all the PPs could simulate SY better than Q in spatial distribution. HRLT has the potential to be used in long-term hydrological simulations in ungauged or small watersheds based on its high spatial resolution compared to other products.

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.

Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data

The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimization, grey wolf optimization, the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), and the jellyfish search optimizer (JFO) in groundwater level estimation. Daily precipitation and temperature datasets acquired from two stations in northern Bangladesh were used as inputs to the models, which were evaluated based on different quantitative statistics and assessed based on RMSE, MAE, R2, and some new graphical inspection methods. The outcomes of the applications revealed that the efficiency of ELM models was considerably improved by using metaheuristic algorithms. The ELM-JSO improved the RMSE of the standalone ELM model by 13% for the optimal precipitation, temperature, and groundwater level inputs in the testing stage. Among the implemented methods, the ELM-JFO performed the best in estimating the daily groundwater level, and the ELM-WOA and ELM-HHO, respectively, followed it. Viability of a new extreme machine learning (ELM) method tuned with Jellyfish search optimizer (JFO) is investigated in groundwater level estimation. The ELM-JFO is compared with hybrid ELM-PSO, ELM-WOA and ELM-HHO models using daily precipitation and temperature data acquired from two stations of Bangladesh. The ELM-JSO improves the root mean square error of the standalone ELM model by 13% for the optimal precipitation, temperature and groundwater level inputs.

Daily High-Resolution Temperature and Precipitation Fields for the Contiguous United States from 1951 to Present

Abstract In this paper, a new set of daily gridded fields and area averages of temperature and precipitation is introduced that covers the contiguous United States (CONUS) from 1951 to present. With daily updates and a grid resolution of approximately 0.0417° (nominally 5 km), the product, named nClimGrid-Daily, is designed to be used particularly in climate monitoring and other applications that rely on placing event-specific meteorological patterns into a long-term historical context. The gridded fields were generated by interpolating morning and midnight observations from the Global Historical Climatology Network–Daily dataset using thin-plate smoothing splines. Additional processing steps limit the adverse effects of spatial and temporal variations in station density, observation time, and other factors on the quality and homogeneity of the fields. The resulting gridded data provide smoothed representations of the point observations, although the accuracy of estimates for individual grid points and days can be sensitive to local spatial variability and the ability of the available observations and interpolation technique to capture that variability. The nClimGrid-Daily dataset is therefore recommended for applications that require the aggregation of estimates in space and/or time, such as climate monitoring analyses at regional to national scales. Significance Statement Many applications that use historical weather observations require data on a high-resolution grid that are updated daily. Here, a new dataset of daily temperature and precipitation for 1951–present is introduced that was created by interpolating irregularly spaced observations to a regular grid with a spacing of 0.0417° across the contiguous United States. Compared to other such datasets, this product is particularly suitable for monitoring climate and drought on a daily basis because it was processed so as to limit artificial variations in space and time that may result from changes in the types and distribution of observations used.

Identifying hot spots of long-duration extreme climate events in the northwest arid region of China and implications for glaciers and runoff

China's Northwest Arid Region (NAR), with dry and cold climate conditions and glaciers widely developed in the high mountains, provides vital water resources for Asia. The consecutive cold, warm, dry and wet days have much higher impacts on the water cycle process in this region than extreme temperature and precipitation events with short durations but high intensities. Parametric and nonparametric trend analysis methods widely used in climatology and hydrology are employed to identify the temporal and spatial features of the changes in the consecutive cold, warm, dry and wet days in the NAR based on China's 0.5° × 0.5° meteorological grid datasets of daily temperature and precipitation from 1961 to 2018. This study found that (1) the consecutive cold days (Cold Spell Duration Indicator, CSDI), and the consecutive dry days (CDD) decreased, while the consecutive warm days (Warm Spell Duration Indicator, WSDI), and the consecutive wet days (CWD) increased from 1961 to 2018, (2) and the eastern Kunlun Mountains were the hot spots where all of these consecutive climate indices changed significantly, (3) and the changes in these consecutive climate indices were highly correlated with the rise in the Global Mean Land/Ocean Temperature Index. The results indicated that winters tended to warmer and dryer and summer became hotter and wetter during 1961–2018 in the NAR under the global warming, which can lead to the sustained glacier retreat and the increase in summer runoff in this region, and the eastern Kunlun Mountains are the area where could face high risks of water scarcity and floods if the changes in these climate indices continue in the future. Given the vulnerability of the socio-economic systems in the NAR to a water shortage and floods, it is most crucial to improve the strategies of water resources management, disaster prevention and risk management for this region under climate change.

Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)

Abstract. High-resolution gridded datasets of meteorological variables are needed in order to resolve fine-scale hydrological gradients in complex mountainous terrain. Across the United States, the highest available spatial resolution of gridded datasets of daily meteorological records is approximately 800 m. This work presents gridded datasets of daily precipitation and mean temperature for the East–Taylor subbasin (in the western United States) covering a 12-year period (2008–2019) at a high spatial resolution (400 m). The datasets are generated using a downscaling framework that uses data-driven models to learn relationships between climate variables and topography. We observe that downscaled datasets of precipitation and mean temperature exhibit smoother spatial gradients (while preserving the spatial variability) when compared to their coarser counterparts. Additionally, we also observe that when downscaled datasets are upscaled to the original resolution (800 m), the mean residual error is almost zero, ensuring no bias when compared with the original data. Furthermore, the downscaled datasets are observed to be linearly related to elevation, which is consistent with the methodology underlying the original 800 m product. Finally, we validate the spatial patterns exhibited by downscaled datasets via an example use case that models lidar-derived estimates of snowpack. The presented dataset constitutes a valuable resource to resolve fine-scale hydrological gradients in the mountainous terrain of the East–Taylor subbasin, which is an important study area in the context of water security for the southwestern United States and Mexico. The dataset is publicly available at https://doi.org/10.15485/1822259 (Mital et al., 2021).

Changes in Meteorological Dry Conditions across Water Management Zones in Uganda

This study analysed trends and variability in extreme precipitation and potential evapotranspiration (PET) indices across Uganda. We made use of daily precipitation and temperature datasets from 1979 to 2013 obtained from the Climate Forecast System Reanalysis (CFSR) products of the National Centres for Environmental Prediction (NCEP). The PET was estimated using the Hargreaves method based on the minimum and maximum temperature. Analysis was focused on the level of Water Management Zones (WMZs). Examples of the extracted extreme climatic indices included the annual maximum number of consecutive days with precipitation intensity < 1 mm/day (CDD1) or < 5 mm/day (CDD5), annual sum of PET for days with PET rate > 5 mm/day (SPETD5), and spell of high evaporative demand (or annual maximum number of consecutive days with PET > 5 mm/day) (CDPET5). Attributes of the variability in meteorological dry conditions were investigated. Trend and variability were investigated using an approach based on the cumulative sum of difference (CSD) between exceedance and non-exceedance counts of data points. The magnitudes of the linear decrease or increase in the precipitation and PET indices were determined using Sen's method. To apply these methods, the CSD-based Sub (Trend) and Variability Analysis Tool (CSD-VAT) was used. The long-term mean of the extracted indices showed Kyoga and Victoria to be the driest and wettest WMZs, respectively. An increase in the number of dry days (indicating increasing severity of meteorological dry conditions) was confined to the Karamoja region or the eastern parts of the Upper Nile and Kyoga WMZs. However, the rest of the country generally experienced negative trends in the precipitation and PET indices. Extreme precipitation indices of Kyoga and the Upper Nile WMZs exhibited consecutive positive and negative sub-trends over the early 1980s and from 1985 to 2013, respectively. Victoria and Albert WMZs mainly exhibited negative sub-trends for both precipitation and PET indices especially from the mid-1980s to the end of the data. Across Victoria and Albert WMZs, we found that the variability in extreme precipitation indices such as CDD1 and CDD5 was significant (p<0.05). However, these WMZs were characterized by insignificant (p>0.05) variability of the PET indices such as SPETD5 and CDPET5. Changes in the precipitation and PET indices were significantly (p<0.05) found to be positively linked to the quasi-biennial oscillation. The meteorological drought indicators were significantly (p<0.05) negatively correlated with the Indian Ocean dipole and Atlantic multidecadal oscillation. Our findings based on NCEP's CFSR data show that variation in sub-trends of the meteorological dry conditions across Uganda resonates well with the changes in some indicators of large-scale ocean-atmosphere conditions. This is important for planning predictive adaptation to the impacts of climate variability on water resources applications which are sensitive to the severity of meteorological dry conditions. Apart from our findings, we introduced a number of extreme PET indices which can be used for analysis of meteorological dry conditions or drought.

Corrected ERA5 Precipitation by Machine Learning Significantly Improved Flow Simulations for the Third Pole Basins

Abstract Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region. Significance Statement The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.

Regional Analysis of Hotspot and Coldspot Areas Undergoing Nonstationary Drought Characteristics in a Changing Climate

Conventional drought indices based on stationary assumptions are no longer appropriate for drought assessments conducted under conditions with climate change or anthropogenic influences. In this study, a time-varying Nonstationary Standardized Precipitation Index (NSPEI) was developed by fitting a time covariate with the Generalized Additive Models for Location, Scale, and Shape (GAMLSS), with a time-scale of six months. Daily precipitation, maximum temperature, and minimum temperature datasets from 1979–2020 that were based on the Climate Prediction Center (CPC) global unified gauge-based analysis with a resolution of 0.5° × 0.5° were used. The results of the study indicated that both precipitation and evapotranspiration in China had increased significantly over the past 42 years in China and that Northwest China would become drier. By extracting the return objects of the GAMLSS, this study identified Northwest China (Continental River Basin) as the main region wherein the distribution parameters of the non-stationary models changed; this region was identified as the one concentrated with nonstationary hotspot response areas. A comparison of drought duration and severity showed that the stationary SPEI under-estimated the severity of the drought. The severity was under-estimated in the spring–summer and fall–winter seasons for Northwest and Southwest of China, respectively; more attention should be paid to these regions. This study provides results that can support nonstationary drought research; droughts can be identified more precisely, and early warnings can be provided for them.

Potential Effects of Urbanization on Precipitation Extremes in the Pearl River Delta Region, China

Rapid urbanization plays an indelible role in modifying local climate, with more extreme precipitation in urban areas. Understanding the mechanism of urban-induced precipitation changes and quantifying the potential effects of urbanization on the changes in precipitation extremes have become hotspot issues in hydrometeorology. We examine the spatiotemporal changes of precipitation extremes over the Pearl River Delta region in China using the homogenized daily precipitation dataset from the period 1961–2017, and quantify the urbanization effects on these changes. Most of the extreme precipitation indices show increasing trends, but only the mean precipitation intensity has a significant increase. Urbanization could induce the intensification of extreme precipitation, with a higher amount, intensity, and frequency of precipitation extremes and a larger magnitude of their trends in urban areas by comparison with those rural areas. Moreover, high-level urbanization tends to make a greater contribution to the temporal changes in precipitation extremes, indicating that urbanization effects on precipitation extremes may be related to urbanization levels. However, urbanization level shows little effect on the changes in the spatial patterns of precipitation extremes, with similar spatial distribution in different urbanization stages. Our findings highlight the important role of urbanization in precipitation extremes and offer insights into the feedback of anthropogenic changes into variations in precipitation extremes.

A high-resolution projected climate dataset for Vietnam: Construction and preliminary application in assessing future change

Abstract This study generates a daily temperature and precipitation dataset over Vietnam at a high resolution of 0.1° for the historical period 1980–2005 and the future period 2006–2100 under four representative concentration pathway (RCP) scenarios, namely RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The bias correction (BC) and spatial disaggregation (SD) method is applied to the outputs of 31 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) to create the new dataset called CMIP5-VN. To guide the BC and SD steps, gridded temperature and precipitation data interpolated from daily observations of 147 and 481 stations respectively are used. Results with the CMIP5-VN show that warming over Vietnam is projected to continue till the end of the 21st century under all four RCPs. The average temperature is projected to increase by 1.3±0.52 °C under RCP2.6 and by 3.85±0.85 °C under RCP8.5 between 2080–2099 and 1986–2005. The future increase is more intense in the northern regions than in the south and higher in summer than in winter. Precipitation is projected to increase by 1.16±7.1% under RCP2.6 and by 4.41±9.2% under RCP8.5. In Central Vietnam, there is a consistent rainfall increase in the future rainy season.

Fronts and Cyclones Associated with Changes in the Total and Extreme Precipitation over China

Abstract Precipitation in China is controlled by a combination of multiple weather systems and is susceptible to their spatiotemporal variability under climate warming. Based on the daily precipitation dataset developed by the China Meteorological Administration and ERA5 6-hourly data from 1979 to 2018, we detected tropical cyclones, extratropical cyclones, and fronts affecting mainland China of an altitude less than 1500 m using objective methods and identified their individually associated precipitation. We quantified climatological characteristics of precipitation associated with these three meteorological causes and their proportion of precipitation totals, and evaluated their relative contribution to change in precipitation. Results show that precipitation associated with tropical cyclones (TCP), extratropical cyclones (ETCP), and fronts (FRTP) accounted for 3.0%, 21.2%, and 20.4% of the total precipitation in China, and constituted the largest percentage in August, June, and January, respectively. The total proportion of these three types of precipitation exceeded 50% in more than 55% of China’s regions. The high-value regions of corresponding precipitation were directly related to the locations of frequent occurrences of these weather systems. TCP and FRTP increased over the past four decades, while ETCP decreased. The western part of Northeast China and the middle and upper reaches of the Yangtze River show precipitation decreases, 37.8% of which is contributed by ETCP decreases. Precipitation increased in most regions of China during the dry season, 34.1% of which is contributed by FRTP increases. Among them, Northeast (Northwest) China significantly increased it precipitation by 14.4% decade−1 (21.8% decade−1), 33.6% (51.3%) of which is contributed by FRTP increases.

Daily precipitation dataset at 0.1° for the Yarlung Zangbo River basin from 2001 to 2015

In order to obtain higher precision regional precipitation dataset in the Yarlung Zangbo River basin, two different schemes were proposed on the basis of the two most application potential satellite-based precipitation products, IMERG and CMORPH_BLD. The first method aimed to correct the positive error of IMERG based on high correlation (CC > 0.9) between IMERG and gauges. The second algorithm was developed to merge IMERG with CMORPH_BLD by the stepwise linear regression. As the reference, IMERG played a key role in correction of precipitation ratio determination and precipitation event detection. Two daily datasets with 0.1° resolution (BRD_IMERG and IGREA_IMERG-CMORPH) performed better than IMERG in CC, RMSE, ME, FAR and CSI, and streamflow simulation in the whole basin (NS: 0.86 and 0.87; RBIAS: −19% and −11%) and sub-basins. The two proposed methods were relatively simple and efficient for reconstructing higher precision regional precipitation, and the datasets provided a good application demonstration in the alpine region.

Performance Assessment of Different Precipitation Databases (Gridded Analyses and Reanalyses) for the New Brazilian Agricultural Frontier: SEALBA

Since the early 2000s, Brazil has been one of the world’s leading grain producers, with agribusiness accounting for around 28% of the Brazilian GDP in 2021. Substantial investments in research, coupled with the expansion of arable areas, owed to the advent of new agriculture frontiers, led the country to become the world’s greatest producer of soybean. One of the newest agricultural frontiers to be emerging in Brazil is the one known as SEALBA, an acronym that refers to the three Brazilian states whose areas it is comprised of—Sergipe, Alagoas, and Bahia—all located in the Northeast region of the country. It is an extensive area with a favorable climate for the production of grains, including soybeans, with a rainy season that takes place in autumn/winter, unlike the Brazilian regions that are currently the main producers of these kinds of crops, in which the rainfall regime has the wet period concentrated in spring/summer. Considering that precipitation is the main determinant climatic factor for crops, the scarcity of weather stations in the SEALBA region poses an obstacle to an accurate evaluation of the actual feasibility of the region to a given crop. Therefore, the aim of this work was to carry out an assessment of the performance of four different precipitation databases of alternative sources to observations: two from gridded analyses, MERGE and CHIRPS, and the other two from ECMWF reanalyses, ERA5, and ERA5Land, and by comparing them to observational records from stations along the region. The analysis was based on a comparison with data from seven weather stations located in SEALBA, in the period 2001–2020, through three dexterity indices: the mean absolute error (MAE), the root mean squared errors (RMSE), and the coefficient of Pearson’s correlation (r), showing that the gridded analyzes performed better than the reanalyses, with MERGE showing the highest correlations and the lowest errors (global average r between stations of 0.96, followed by CHIRPS with 0.85, ERA5Land with 0.83, and ERA5 with 0.70; average MAE 14.3 mm, followed by CHIRPS with 21.3 mm, ERA5Land with 42.1 mm and ERA5 with 50.1 mm; average RMSE between stations of 24.6 mm, followed by CHIRPS with 50.8 mm, ERA5Land with 62.3 mm and ERA5 with 71.4 mm). Since all databases provide up-to-date data, our findings indicate that, for any research that needs a complete daily precipitation dataset for the SEALBA region, preference should be given to use the data in the following order of priority: MERGE, CHIRPS, ERA5Land, and ERA5.

Use of daily precipitation records to assess the response of extreme events to global warming: Methodology and illustrative application to the European region

AbstractWe introduce a technique for identifying daily precipitation records based on the analysis of maximum daily precipitation events within running 30‐day periods. The technique is applied to the E‐OBS daily precipitation dataset for the European region spanning the period 1950–2018. We find that, on a Europe wide scale, the number of actual precipitation records as defined above exceeds the theoretical value expected under stationary climate conditions by a factor increasing with time by up to 1.6–1.8. This result, which has substantial subregional variability, is indicative of an increase of daily precipitation extremes consistent with what expected from climate warming conditions. We also apply the technique to the ERA5 reanalysis and four illustrative 21st century projections conducted with two regional climate models under the high end RCP8.5 and low end RCP2.6 greenhouse gas concentration pathways. We find a sustained increase of precipitation records throughout the 21st century with respect to the expected values for the RCP8.5 scenario, and a much reduced signal for the RCP2.6. In both cases the overall increase in actual to expected ratios of precipitation records is consistent with the warming trends found in the two scenarios, although this signal is highly variable in space and depends on the model being analysed. Our technique can be useful in studies of detection and attribution of extreme precipitation events and in the assessment of the impacts of increasing extremes under global warming conditions.

Inter-Annual Variations of Precipitation Modulate the Dry Spell Length.

Dry spell length (DSL), consecutive non‐rainy days between two precipitation events, play an important role in regulating soil moisture dynamics, terrestrial energy exchange as well as vegetation growth. According to the Clausius‐Clapeyron (C‐C) relationship, global warming can result in prolonged DSL. However, usually the amount of precipitation and its characteristics coincidentally varied with the changes of DSL under global warming, it remains unclear how the inter‐annual variation of precipitation interacts with the evolution of dry spells. In this study, the global long‐term in‐situ observation data set of daily precipitation during 1976–2019 was used to examine the spatiotemporal trends of growing season DSL and precipitation. Our results showed that the global mean growing season DSL significantly increased by 0.3 days decade−1 during 1976–1998 while no significant trend of that was observed during 1999–2019. In contrast, the growing season precipitation (Prec_GS) showed no significant trend in 1976–1998 whereas significant increase trend of that was observed in 1999–2019. To explore the impacts of precipitation on the evolution of dry spells, we examined the relationship between the growing season DSL and Prec_GS. We found that prevalent negative relationship was observed between growing season DSL and Prec_GS in 88% and 86% stations during the period of 1976–1998 and 1999–2019, respectively. Spatially, the mean annual Prec_GS and DSL showed significantly negative relationship, that is, the stations with more precipitation showed shorter DSL in growing season, and vice versa. The changes of mean annual Prec_GS explained 81% spatial variation of growing season DSL. Moreover, during the period of 1999–2019 significant increase of precipitation frequency and decrease of dry day frequency were also observed in addition to the increase of Prec_GS in this period. The decreased dry day frequency further resulted in the decrease of growing season DSL. By excluded the impacts of precipitation, the DSL/Prec_GS ratio showed significant decreasing trend during 1999–2019. Our study suggested that the spatiotemporal variations of DSL were modulated by the variation of precipitation. The impacts of precipitation changes on ecosystem by altering the dry spell evolution should be considered in modeling the terrestrial carbon and hydrological cycling in response to climate changes.