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Moisture sources and transport processes play a critical part in hydrological cycle and determine regional precipitation. This paper utilizes the Water Accounting Model‐2layers (WAM‐2layers) and the ERA5 reanalysis data to track the sources of precipitation over the Pearl River Basin (PRB). The contribution of external moisture and the role of local recycling are investigated. The results show that during the period from 1980 to 2020, oceanic sources including the western North Pacific and Indian Oceans serve as the primary moisture sources of precipitation over the PRB. The contributions to total seasonal precipitation are respectively 62.57% in MAM, 54.79% in JJA, 43.70% in SON and 60.88% in DJF. By contrast, the contribution of local recycling is generally below 5.50%. In the dry years of 1994, 1997 and 2001, the contribution of terrestrial sources is about 19.22%; in the wet years of 1989, 2009 and 2011, the contribution is about 16.31%. The summer precipitation anomalies are mainly attributable to moisture anomalies from the Equatorial Indian Ocean in the wet years and from Southeast Asia in the dry years. Furthermore, vertically integrated moisture flux anomalies over the boundaries of the PRB are generally the result of anomalous wind rather than anomalous moisture. In the wet years, low‐pressure systems induce strong cyclonic moisture transports, increasing the PRB precipitation. In the dry years, high‐pressure anomalies over the PRB block the moisture transports from the Indian Ocean and western North Pacific. [ABSTRACT FROM AUTHOR]

The South China Sea summer monsoon (SCSSM) onset and withdrawal have critical impacts on the climate of East Asia. However, using different reanalysis data may lead to the uncertainty of the SCSSM. This study uses reanalysis datasets including JRA‐55, ERA5, NCEP‐NCAR NCEP‐1 and NCEP‐DOE NCEP‐2 to calculate onset and withdrawal dates and index of SCSSM from 1991 to 2020, analyses the uncertainty and figures out the possible reasons. Results reveal that the four reanalysis datasets have obvious uncertainty in calculating the withdrawal date of SCSSM, with the deviation being up to 3.4 pentads. Further analysis shows that large differences in thermal conditions and lead to the uncertainties. From the perspective of precipitation, Greater uncertainty exists in the relationship between monsoon retreat and September–October precipitation, NCEP1 and NCEP2 datasets show weak correlation in southern China and Indonesia. Moreover, the uncertainty can be also found in the correlations of SCSSM with global sea surface temperature (SST) anomalies. JRA‐55 and ERA5 show a strong correlation of with SST in several areas, and exhibit a distinct Pacific decadal oscillation (PDO) mode with monsoon onset, while NCEP‐1 and NCEP‐2 have a weaker correlation with SST anomalies than JRA‐55 or ERA5. Comparing multiple factors comprehensively, this paper concludes that JRA‐55 may be more representative in the monsoon study, followed by ERA data. In short, depending on the specific research questions, researchers can choose appropriate data sources to study the SCSSM based on the different aspects and the uncertainty also deserve our special attention when selecting the reanalysis datasets. [ABSTRACT FROM AUTHOR]

Climate is a powerful driver of agricultural and natural systems, and spatial climate datasets are currently in great demand. This is especially true in the Arequipa Department of Peru, a region with low seasonal precipitation, remarkable topographic variability, and significant water demand in a highly managed water system. This paper presents the Arequipa Climate Maps (ACM) datasets, a high resolution (1 km) spatial 30‐year (1988–2017) climate dataset for the Arequipa Region, in Peru. Four interpolation methods, and combinations of those methods, were tested to produce 30 years of daily precipitation, maximum and minimum air temperature: Ordinary Kriging (OK), Thin Plate Splines (TPS), Regression Kriging (RK), and Regression Thin Plate Splines (RTPS). The mixed method RTPS‐TPS and RTPS using locally fitted polynomial and potential regressions were found to best represent the spatial variability of precipitation and daily extreme temperatures, respectively, and helped compensate the bias resulting from the lack of weather stations at higher elevations. These methods were then selected to create the ACM dataset, which contains climate maps of 30‐year annual and monthly climate normals (ACM–Normals) and 30 years of annual, monthly, and daily climate maps (ACM–YMD). In addition, insights on weather station gap filling in mountainous areas and bias corrections for avoidance of anomalous precipitation and to assure consistency between annual, monthly and daily data are presented, together with discussion about the quality and limitations of the dataset, and its comparison with other datasets. [ABSTRACT FROM AUTHOR]

Increasing variability of precipitation induced by climate change raises uncertainties for ecosystem services and agricultural production in the Poyang Lake Basin (PLB), China. Reanalysis precipitation datasets (RPDs) are attractive alternatives for monitoring the hydro‐climate cycles. However, their applicability in China has mainly been evaluated at the national scale, while less attention has been paid to the PLB or southeastern China. Here, we apply various metrics to evaluate the magnitude and spatial similarity of annual, monthly, different intensities and the spatial structure of precipitation from four reanalysis—the fifth global atmospheric analysis dataset of the European Center for Medium‐Range Weather Forecasts (ECMWF) (ERA5), Interim ECMWF Reanalysis (ERA‐Interim), Japanese 55‐year Reanalysis project (JRA55) and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2), against the rain‐gauges over the PLB during 1980–2018. The results showed that the detection capacity of ERA5 and MERRA2 shifted around 2002, whereas that of ERA‐Interim and JRA55 did not substantially change. Before 2002, ERA‐Interim had the lowest biases for the magnitude of annual, monthly and different intensities of precipitation (except for heavy rain), while ERA5 and MERRA2 had strengths in capturing spatial correlation. Notably, ERA5 and MERRA2 had region‐wide wet and dry biases, respectively, before 2002, which can be attributed mainly to the estimation of flood season precipitation (from April to June) and heavy rain. In contrast, the fractional contribution of light–moderate rain was underestimated and overestimated by ERA5 and MERRA2, respectively. However, after 2002, because of the improved reproducibility of flood season precipitation and heavy rain magnitude, the systematic biases of ERA5 and MERRA2 significantly decreased, especially for ERA5, which outperformed other RPDs on all aspects of precipitation. The conclusions of this paper suggest that the variation of applicability over time should be seriously tested before the utilization of RPDs in hydro‐climate analyses. [ABSTRACT FROM AUTHOR]

Monsoon and its teleconnection with earth system internal processes affect the spatiotemporal distribution of precipitation and water resources. In this paper, the wavelet coherence analysis has been utilized, a time and frequency domain methodology for comparing the spectral features of two independent time series superior to linear approaches. This technique is used to capture the significant modes of variabilities in the Indian Summer Monsoon Index (ISMI) and large‐scale climate indices (CIs) between ocean–atmosphere oscillations, like Indian Ocean Dipole (IOD), El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) over Pakistan. Precipitation time series during 1960–2016 revealed significant interannual coherences with ISMI, whereas the remaining CIs (IOD, ENSO, PDO, SOI, NAO, AMO, and AO) revealed interannual, decadal and interdecadal coherences. However, AO revealed strongest coherences in R‐II, III, and VI at interdecadal scales among all CIs. Overall, the interannual cycles on ISMI are 2.8 years, IOD 1–5.3 years, PDO 0–5.3 years, SOI 1–5.3 years, NAO 0–5 years, AO 0–5 years, and AMO 0–8.3 years. Whereas, the remaining CIs shared interdecadal coherences over particular regions. The ISMI displayed coherences (except in the UIB) with the large‐scale CIs over various homogenous regions on an interannual scale. The dominant influence of ISMI is observed in R‐II and III; the significant coherences in R‐II ranged from ~8 to 32 months (~0.8–2.8 years). The IOD and NAO have major coherences than the remaining large‐scale CIs ranging from ~16 to 64 months (1.3–5.3 years). The AO has the most significant coherences observed in R‐II, III, and VI on the decadal/interdecadal scale from 128 months and above (almost 10–15 years). On a 1.0‐year timescale, all homogenous regions demonstrated strong intermittent coherence with ISMI, IOD, ENSO, PDO, SOI, NAO, AMO, and AO. These findings have substantial implications for decision‐makers and scientists in Pakistan looking to enhance water resource planning and operations in the face of future climate uncertainties. [ABSTRACT FROM AUTHOR]

Monitoring and quantifying high‐latitude atmospheric circulation variability and trends are important towards understanding the anatomy of extreme events and constraining their probabilities under continued Arctic change. The greater Alaska region stands out as one region of enhanced warming and environmental changes over the Arctic amplification era, which has shown proclivity to extreme events and speaks to the need to consistently monitor overlying atmospheric variability. In this paper, we describe the creation and analysis of an updated, daily mid‐tropospheric Alaska Blocking Index (ABI) time series from 1948 to 2020. Sensitivity testing and index modifications from the initial dataset are discussed, and the new ABI is evaluated over the full period and subperiods. Additionally, we assess the role of the ABI as a regional climate indicator by analysing its variability against surface air temperature and precipitation from 13 climate divisions across Alaska as well as broader‐scale hemispheric temperatures. Months of the highest ABI means mainly transpired in the most recent climatological period, 1991–2020, and most notably during the spring and autumn transition seasons, reflecting the nonstationary nature of the jet stream magnitude through time atop Alaska. ABI trends are positive across all months, seasons, and annually only in the 1991–2020 period. Contemporaneous ABI values are strongly and positively correlated with air temperature across all Alaskan climate divisions during the warm season, but negatively correlated with winter temperatures over southeastern Alaska. Meanwhile, the ABI is significantly anticorrelated with summer precipitation over Interior and southern Alaska, but only over southeastern Alaska in other seasons. The ABI is statistically differentiated from the primary modes of atmospheric variability as shown by its generally weak correlations with the Arctic Dipole and the Arctic Oscillation (r ≤ 0.40). [ABSTRACT FROM AUTHOR]

Climate models' capability of reproducing the present climate at both global and regional scales still needs improvements. The assessment of model performance critically depends on the data sets used as comparators/references. Reanalysis and gridded observational data sets have been frequently used for this purpose. However, none of these can be considered an accurate reference data set because of their associated uncertainties and full representativity. This paper, for the first time, uses in‐situ measurements from National Oceanic and Atmospheric Administration U.S. Climate Reference Network (USCRN) spanning the period 2006–2020 to assess daily temperature and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA‐Interim) involved in NA‐CORDEX. The assessment is also extended to the most recent and widely used Earth system reanalyses (ERA5, ERA‐Interim, MERRA2 and NARR) and a few in situ‐based gridded data sets (Daymet, PRISM, Livneh and CPC). Results show that biases for the different data sets are seasonally and subregionally dependent. On average, reanalysis and in situ‐based gridded data sets are warmer (with biases exceeding 0.3°C) than USCRN year‐round, while RCMs are colder (warmer) in winter (summer) with biases ranging from −0.5 (0.9)°C for RCMs at 0.44° to −0.2 (1.4)°C for CRCM5‐UQAM‐11. In situ‐based gridded data sets provide the best performance in most of the Contiguous United States (CONUS) regions compared to reanalyses and RCMs, but still have biases in regions such as the Western mountains and the Pacific Northwest. Furthermore, in most US subregions, reanalysis data sets do not outperform reanalysis‐driven RCMs. Likewise, for both reanalysis data sets and RCMs, temperature and precipitation biases vary considerably depending on the local orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis. [ABSTRACT FROM AUTHOR]

When assessing long‐term climate trends and variability, it is important to analyse data that are not disturbed by external factors that might lead to misleading trends. It is also urgent to analyse consistent series that cover the entire time period. Since observation networks are continuously changing not too many complete series are available for a centennial long analysis. In this paper an approach to construct long‐term homogenized climate series to be used to derive a long‐term gridded dataset is presented. A gridded dataset of monthly climate anomalies based on all available observations was used to construct 165 raw temperature series and 323 raw precipitation series covering the entire period 1901–2020. These series are homogenized by using the automatic procedure in the R‐package climatol. The gridded data based on the homogenized data show the same regional trends and variability as the data derived from non‐homogenized data. The local spatial variance is smaller in the gridded data based on homogenized series than those based on the raw data series. Both the construction and homogenization of data series to provide spatial and temporal consistent data for gridding lead to better and more reliable gridded datasets for analysing climate trends and variability. [ABSTRACT FROM AUTHOR]

The homogenization of climate observational series is a needed process before undertaking confidently any study of their internal variability, since changes in the observation methods or in the surroundings of the observatories, for instance, can introduce biases in the data of the same order of magnitude than the underlying climate variations and trends. Many methods have been proposed in the past to remove the unwanted perturbations from the climatic series, and some of them have been implemented in software packages freely available on the Internet. The Spanish project MULTITEST was intended to test their performance in an automatic way with synthetic monthly series of air temperature and atmospheric precipitation, to update inter‐comparison results from former projects, especially those of the COST Action ES0601. Several networks representing different climates and station densities were used to test a variety of homogenization packages on hundreds of random samples. Results were evaluated mainly in the form of root mean squared errors and errors in the trend of the series, showing that ACMANT, followed by Climatol, minimized these errors. However, other packages performed also relatively well, even outperforming them when there were simultaneous biases of the same sign in most or all the test series. [ABSTRACT FROM AUTHOR]

Warming has already changed the patterns of precipitation and increased the risk of extreme climate events. An assessment of the influences from anthropogenic and natural forcings on future changes in precipitation is therefore essential to define appropriate mitigation and adaptation policies. We assess the effects of natural (NAT) and anthropogenic forcings (greenhouse gases [GHG] and anthropogenic aerosols [AA]) on precipitation over land areas simulated under the Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP2‐4.5 scenario. Our results indicate that changes in precipitation due to GHG forcing, AA forcing and all external forcings (ALL) are projected to increase by about 3.0%–24.8%, 1.1%–6.9% and 4.2%–31.2%, respectively, across most land areas in the twenty‐first century, especially over the mid‐ and high latitudes of the Northern Hemisphere and central Africa, with an increase in the mean and flattening of the probability distribution functions (PDFs). By contrast, the NAT‐induced changes are relatively small, with a slight increase in the mean of the PDFs. The frequencies of both future extreme precipitation and dry events are projected to increase by 11%–101% and 3%–72%, respectively, over many continental regions under the influence of external forcings, especially under GHG forcing. Fingerprinting detection shows that the signals of GHG (except for Australia), AA and ALL forcing are detectable at the likely level (signal‐to‐noise ratio >0.95; 66% confidence) over most land areas. The GHG signals are stronger and detected earlier (after 2010s, likely level) than the AA signals (after 2060s, likely level). [ABSTRACT FROM AUTHOR]

The fragile ecological environment of the Mongolian Plateau (MP) is sensitive to climate change. It is necessary to fully understand the temperature and precipitation changes on the MP to ensure regional sustainable development. Most existing studies are conducted based on station observations which suffer from sparse distribution and limited spatial representativeness and cannot perfectly represent the climatic conditions of the whole region. By contrast, the long‐term, spatially continuous reanalysis products offer new opportunities for MP climate change research. However, the coarse spatial resolution and large uncertainties of reanalysis data limit their applicability to provide reliable climate information at finer scales. Within the delta downscaling framework, we downscale and correct the state‐of‐the‐art European Centre for Medium‐range Weather Forecasts ReAnalysis 5 land portion (ERA5‐Land) using a high‐resolution WorldClim reference climatology and generate a long‐term (1950–2020) 1‐km monthly air temperature and precipitation downscaled dataset. During the downscaling process, the results obtained by nearest neighbour, bilinear and bicubic interpolation methods are evaluated and compared. Air temperature result of bilinear method and precipitation result of nearest neighbour method, which have the best accuracy, are taken as the final downscaled data. The evaluation shows that the downscaled data has acceptable accuracy (overall MBE = −0.41°C, Corr = 0.997, RMSE = 1.51°C for air temperature, and MBE = 1.58 mm·month−1, Corr = 0.747, RMSE = 24.24 mm·month−1 for precipitation), and outperforms the original ERA5‐Land and widely used Climatic Research Unit (CRU) data. In addition, the downscaled data can also provide accurate and detailed temperature and precipitation change trends over the MP. With fine spatial resolution, long‐time span and good spatial continuity, the downscaled air temperature and precipitation will be a useful data source to explore climate change over the MP. [ABSTRACT FROM AUTHOR]

Five regional climate models (RCMs)—CCLM, RegCM, HadGEM3‐RA, SNURCM and WRF—participating in the Coordinated Regional Climate Downscaling Experiment‐East Asia (CORDEX‐EA) Phase 2 project are evaluated for their ability to simulate spatiotemporal variability in Asian summer precipitation. For this purpose, two dynamical downscaling sets, experiments forced by ERA‐Interim reanalysis data (reproduction experiment) and historical data from three Coupled Model Intercomparison Project 5 (CMIP5) general circulation models (GCMs) (historical experiment) are analysed. The horizontal resolution of the downscaled results is 25 km, and the analysis period is from 1981 to 2005 (25 years). The RCMs show reasonable performance in simulating the spatial and temporal characteristics of summer precipitation in CORDEX‐EA Phase 2 domain. To assess spatiotemporal patterns in Asian precipitation, cyclostationary empirical orthogonal function (CSEOF) analysis is used. In the first mode representing seasonal variations, the model ensemble results of both the reproduction experiment (Rep_ENS) and the historical experiment (His_ENS) simulate the peak times, location of precipitation and progression of the monsoons in Asia reasonably. Their features are greatly influenced by the moisture flux, indicating that the relation between precipitation and 850 hPa moisture flux is depicted well by RCM ensembles. In the second mode, which represents intraseasonal variations, the Rep_ENS depicts the western North Pacific summer monsoon break (WNPSM break) phenomenon similar to the observation. Although the His_ENS simulates the WNPSM break later than the observation, it does present the intraseasonal variation in the East Asian summer monsoon region. Based on these results, the five RCM ensemble results show the ability to capture spatiotemporal characteristics of summer precipitation in CORDEX‐EA Phase 2 domain, as well as added value from dynamical downscaling. [ABSTRACT FROM AUTHOR]

This study examines the Interdecadal Pacific Oscillation (IPO) modulation of the El Niño–Southern Oscillation (ENSO) teleconnections in its decaying stages with the tropical ocean by focusing on the Indian Ocean Basin‐Wide (IOBW) mode and the precipitation over South America (SA) in the 1901–2012 period. Composite analyses revealed that the ENSO teleconnections are IPO‐modulated due to the differential ENSO decaying speed, which is slower during the positive than negative IPO phase, for both El Niño (EN) and La Niña (LN) cases. Negative precipitation anomalies related to EN persist over northeastern SA until austral winter for the positive IPO phase (POS IPO), while significant opposite sign anomalies occur in this region for the negative IPO phase (NEG IPO). These results are associated with the Walker circulation's reversal during NEG IPO which is, in turn, accompanied by negative IOBW. During the POS IPO, the positive IOBW causes upward movements over there and, by continuity, downward movements over SA. In the NEG IPO, for LN events the wave train originating in the north of Australia propagates toward subtropical SA, which, coupled with the surface circulation, causes dryness in this region. In addition, the rapid decay of LN in the NEG IPO, followed by the emergence of EN, caused changes in the Walker circulation, such that enhanced upward movements occurred over the Pacific and SA region, and downward movements over the Indian Ocean until austral winter. In turn, the slower decay of the LN in the POS IPO maintains strong subsidence over the central Pacific and weak upward motions over western SA. So, the EN (LN) and positive (negative) IOBW during the POS (NEG) IPO prolong the scarcity of precipitation over equatorial (subtropical) SA. Persistent dry periods over these regions during the ENSO decaying stage might have important implications for the seasonal forecasts. [ABSTRACT FROM AUTHOR]

Besides the respective importance of the frequency, intensity and diurnal cycle of precipitation, the diurnal cycle of intensity–frequency structure could reveal more physical details about the evolution process of precipitation systems. Here, an objective method to quantitatively depict the diurnal cycle of intensity–frequency structure of precipitation is proposed, and applied to complex terrains of the southeastern Tibetan Plateau (TP) and its surroundings. Results show the diurnal cycle of precipitation over the high terrain has a unimodal night peak, while low terrain with a frequency‐dominated night peak and an intensity‐dominated afternoon peak. Both weak and intense precipitation are more frequent at night than those in the afternoon over unimodal regions compared to bimodal regions. Further analysis reveals different diurnal evolution process of intensity–frequency structure within unimodal and bimodal regions, respectively, which reflects different spatiotemporal traits and evolution process of precipitation. Among unimodal regions, the elevated southeastern TP is featured by the synchronous decrease in both weak and intense precipitation from the night peak to the afternoon minimum. While the decrease of total precipitation in regions with lower terrain is dominated by the decrease of intense precipitation, but with an increase in weak precipitation simultaneously. Regarding bimodal regions, the intense precipitation over southern Hengduan Mountains has a more prominent role in leading the diurnal cycle of precipitation amount, compared with that over the southeastern Yungui Plateau. The proposed method provides a new perspective with more physical details to investigate precipitation characteristics in complex terrains, which could be further used to reasonably evaluate and improve the performance of numerical models. [ABSTRACT FROM AUTHOR]

Various climatic factors and their interactions affect China's snow depth alterations. However, a measure of the relative importance of climatic factors regarding the variance in snow depth is lacking. Here, we use a heretofore largest dataset, quality‐controlled daily observations from 1,665 meteorological stations, and statistical analyses to identify proxy drivers and their contributions regarding regional variability and trends in China's snow depth and snow depth‐climate relationships from 1951 to 2019. Results show that seasonal snow depth alterations are substantial in China and caused by various climatic drivers. In winter, observed positive trends in the northwest and northeast of China mainly respond to augmentation in solid precipitation and snow accumulation in fall. For the Tibetan Plateau, the negative snow depth anomaly seems to respond to the rising air temperature below the freezing point along with some secular changes in solid precipitation and shallower fall snowpacks. All these trends in winter snow depth over the northwest, northeast, and Tibetan Plateau of China account approximately for 30% of the total interannual variance in spring snow depth in these regions. In the milder central north and south of China, snow accumulation is not evident. Besides solid precipitation and rising air temperature below the freezing point, atmospheric humidity is also notable for its effect on the interannual variance in winter snow depth in these regions. In spring and fall, however, the negative effect of warming climate on snow depth becomes remarkably more important. It seems that about 13–33% (12–50%) of the total interannual variance in the widespread decrease (insignificant perturbation), except for some weak increases in the northeast region (decreases in the Tibetan Plateau), of China's spring (fall) snow depth are caused by the rising air temperature below the freezing point. [ABSTRACT FROM AUTHOR]

The ERA5 global reanalysis has been compared against a high‐resolution regional reanalysis (COSMO‐REA6) by means of scale‐separation diagnostics based on 2d Haar discrete wavelet transforms. The presented method builds upon existing methods and enables the assessment of bias, error and skill for individual spatial scales, separately. A new skill score (evaluated against random chance) and the Symmetric Bounded Efficiency are introduced. These are compared to the Nash‐Sutcliffe and the Kling‐Gupta Efficiencies, evaluated on different scales, and the benefits of symmetric statistics are illustrated. As expected, the wavelet statistics show that the coarser resolution ERA5 products underestimate small‐to‐medium scale precipitation compared to COSMO‐REA6. The newly introduced skill score shows that the ERA5 control member (EA‐HRES), despite its higher variability, exhibits better skill in representing small‐to‐medium scales with respect to the smoother ensemble members. The Symmetric Bounded Efficiency is suitable for the inter‐comparison of reanalyses, since it is invariant with respect to the order of comparison. [ABSTRACT FROM AUTHOR]

Seasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting. [ABSTRACT FROM AUTHOR]

The Qinghai‐Tibet Plateau (QTP) plays a crucial role in regulating the climate and hydrology over Asia, and studies indicate that the QTP tends to be warm and humid as a whole. Although precipitation variability on the QTP has a great impact on floods, soil erosion, and droughts, it is poorly understood owing to sparse meteorological observations. This study has investigated the temporal variability of precipitation on the QTP and its surrounding areas on different time scales over the past 40 years using the standardized variability index (SVI) based on entropy as well as its changing trends. The results show that the variability of precipitation has different spatial patterns on different time scales. In general, the variability of both the precipitation days and amounts are low in the southeast and northwest corners of the plateau and high in the southwest and north. It was found that the number of light precipitation days in the high vegetation coverage area in the southeastern plateau decreased significantly, but increased greatly in the bare land and sparse vegetation coverage areas in the northern part of the plateau. Such changes are believed to be conducive for ecological restoration. Most importantly, in the southern foothills of the Himalayas, many light precipitation days have turned into moderate and heavy precipitation days, and a significant increase in annual precipitation has also been noted, which are likely to cause natural disasters considering the local topographical conditions, such as landslides and mudslides. Highlights: In QTP, SVIME in May is closer to summer mode, indicating monsoon beginning have advanced to May.Extreme precipitation significantly influences precipitation variability.Daily SVIAE decreases in grassland/barren/sparsely vegetated land, good for ecology.Light rain days increased in bare land/sparse vegetation areas, benefiting vegetation restoration.In southern Himalayas, light rain changed to moderate/heavy rain, leading to natural disasters. [ABSTRACT FROM AUTHOR]

Understanding and exploring hydrological (H) or soil moisture (SM) drought due to meteorological (M) drought under changing climate is crucial for drought early warning. Previously, different methods were used to calculate drought propagation from one state (M to another [H or SM]). However, each method has its pros and cons and thus cannot describe appropriate propagation attributes. In this study, a time series analysis is carried out to explore the propagation process in the Yellow River Basin (YRB) and Hai River Basin (HRB) of China. Standardized Streamflow Index (SSI), Standardized SM Index (SSMI), and Standardized Precipitation Index (SPI) 1‐month time series were calculated using streamflow, satellite‐based SM, and precipitation from 1979 to 2016. The time series framework identifies the average drought propagation as 3.4–8.3 months (M–H) and 2.3–5 months (M–SM) in YRB, while 2.8–7.7 months (M–H) and 2.3–8.5 months (M–SM) in HRB. Cross‐wavelet analysis indicated that periodic characteristics of M drought are responsible for these droughts. Overall, the findings of this study may help to minimize the drought hazards posed by M droughts. [ABSTRACT FROM AUTHOR]

Despite the availability of global and continental climate datasets and climate change information, locally relevant quantification of historic trends in climate variables is still lacking in developing countries, especially at local scales. This is particularly true in the Department of Arequipa, Peru. An arid region with a booming population, substantial mining activities, and large irrigated agriculture, which is highly susceptible to climate change. This study aims to evaluate climate trends from 1988 to 2017 in the Arequipa Department and provide information that can facilitate stakeholders' adaptation to the rapid‐changing climate. The daily precipitation (Prec), and maximum (Tmax) and minimum (Tmin) daily air temperature data used in this study came from the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) and the National Ocean and Atmospheric Administration's (NOAA) Global Summary of the Day (GSOD). Data passed through a quality checking process for removal of implausible data, data gap filling, and inhomogeneity detection. The Mann–Kendall test, at a significance level of 0.10, was used to determine trends and the Theil–Sen slope (Sen's slope) was used to estimate the magnitude of the change. Sen's slope was also calculated spatially, using the gridded Arequipa Climate Maps (ACM) dataset. Results indicate that precipitation seasonality has been increasing, as the observed increase in annual precipitation is happening mostly in the rainy season (December–March) and the start and end of the rainy season are delayed. Positive temperature trends were dominant in the whole region. Tmin is increasing more than Tmax, especially at higher altitudes. Exceptions to increasing temperatures were found in areas influenced by irrigation projects that underwent great expansion. The effect of increasing temperature on glaciers was evaluated by mapping the change in the area with average annual temperature below 0°C between the decades of 1988–1997 and 2008–2017, which reduced by 73.2%, with small areas disappearing and larger contiguous areas shrinking. [ABSTRACT FROM AUTHOR]

Agriculture and hydrological management in western and northwestern Iran is highly sensitive to precipitation associated with Mediterranean and Sudanese cyclones particularly during spring and winter. Although many studies have focused on the theoretical basis of these cyclones, statistical analyses, especially with satellite observations, have not been undertaken. In this study, 40 cyclones during all months of 2015–2021 are characterized using data collected by Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR), Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and ERA5 reanalysis within each cyclone quadrant. The results show that about 45% of cyclones have a lifetime between 1 and 2 days, and approximately 25% occur during April with a secondary peak in March (22.5%). Accordingly, cyclonic rainy days and rainfall amounts peak in April with values of 76.1% and 64.1%, respectively. The lowest (most negative) low‐ and mid‐tropospheric vertical velocity in the southeast and northeast quadrants indicates upward motion, which leads to the most intense latent heat release within the cyclone. Accordingly, the highest rainfall rates occur in the southeast and northeast quadrants, located within and ahead of the highest specific humidity quadrant (southeast) where the warm conveyor belt plays a significant role for rainfall formation. Maximum stratiform and convective rainfall rates are observed in the southwest and southeast quadrants, respectively. The distribution of rainfall rate is consistent with that of the storm‐top height pattern in all quadrants. The maximum percentage of warm rain to the total rain (34.6% on average) for both stratiform and convective precipitation falls in the southeast quadrant. The structure of these analysed cyclones is largely similar to that shown previously in the literature. According to the analysed rainfall in different life cycles, it is expected that the cyclones have the greatest impact (i.e., extreme rainfall) during the developing stage in the northeast and southeast quadrants, and during the mature stage in the southwest quadrant. The lowest influence is during the dissipating stage. The results from this study can help decision makers within the hydrological and risk management industries. [ABSTRACT FROM AUTHOR]

Weather types (WTs) are often used to assess the relationships between synoptic‐scale atmospheric dynamics and local scale climate anomalies. We focus here on Aotearoa New Zealand (ANZ) where pre‐existing WTs have recently been characterized using a set of descriptors monitoring the daily location and intensity of their main atmospheric centres of action (ACAs). We show here that the precipitation and temperature anomalies associated with the WTs in ANZ are more complex than previously thought. They do not solely depend on type occurrence, but are also strongly modulated by within‐type changes in the location and intensity of ACAs, and their interaction with surface terrain. Thus, the mere analysis of WT occurrence is not sufficient to explain climate variability over ANZ. The magnitude and sign of daily minimum and maximum temperature anomalies are strongly driven by meridional advection of temperature, which is also driven by within‐type changes in ACAs. The amplitude of precipitation anomalies is likewise strongly variable within WTs. Associated mechanisms involve interactions between the underlying terrain and atmospheric fluxes modulated by ACAs, modifying the strength and location of orographic uplift. Finally, daily extreme precipitation can occur under several cyclonic WTs, more particularly when their low‐pressure ACAs are significantly more intense than normal. This situation is associated with more intense moisture transport towards ANZ, promoting heavy precipitation there. These results provide a detailed assessment of within‐type variability in temperature and precipitation patterns at both synoptic and local scales. They also help to improve understanding of local effects of larger scale synoptic patterns, thereby allowing for more accurate use of analogues in forecasting. [ABSTRACT FROM AUTHOR]

Rainfall mechanisms in the Central American Isthmus are controlled by complex physical interactions across spatial and temporal scales, which are reflected on the dynamics of atmospheric circulation patterns affecting the region. However, physical mechanisms and their relationships with thermodynamic distributions connected to overturning circulations remain elusive. Here, a set of six recurrent daily atmospheric patterns, or weather types (WT), is defined using a k‐means++ clustering algorithm on standardized fields of Convective Available Potential Energy (CAPE) and winds at 925, 850, and 200 hPa. The relationships between these weather types, their temporal characteristics, and anomalous distributions of moisture flux divergence, equivalent potential temperature (saturated and unsaturated), and observed rainfall are used to describe physical processes controlling the latter, for all seasons. Regional observed rainfall is analysed from a set of 174 automatic stations from all countries from Mexico to Panama. By modulating vertically integrated moisture fluxes, these weather types, and the different climate drivers linked to them, control the temporal and spatial rainfall characteristics in the region, especially over the Pacific side of the isthmus. During some stages of the regional rainy season, described by two weather types, thermal anomalies in convective quasi‐equilibrium characteristic of the upward branch of the Hadley cell force westerly flow over Central America, enhancing rainfall. While during other stages, the enhancement of the trades and the displacement of convection to the ITCZ area over the eastern tropical Pacific, characteristic of the midsummer drought, diminishes rainfall. This study sets the stage for a better understanding of the mechanistic relationship between these weather types and rainfall characteristics in general, like onset, demise, and duration of rainy seasons. Hence, these results can inform process‐based model diagnostics aiming at bias‐correcting climate predictions at multiple timescales. [ABSTRACT FROM AUTHOR]

The overall performance of the simulated seasonal precipitation response to local terrestrial forcings, namely vegetation abundance and soil moisture, in the Sahel among the Coupled Model Intercomparison Project Phase Five (CMIP5) Earth System Models (ESMs) is systematically investigated and compared with its observational counterpart using a multivariate statistical method. The observed seasonal precipitation response is evaluated against a large ensemble of observational, reanalysis, and satellite data sets to provide quantification of uncertainties. The behaviour of models with and without a Dynamic Global Vegetation Model (DGVM) component is also explored, along with the mechanisms responsible for terrestrial feedback on rainfall. In general, the CMIP5 models can reasonably capture the seasonal evolution of Sahel precipitation and soil moisture, albeit with wet biases during the pre‐monsoon period and dry biases during the peak monsoon period. The non‐DGVM ESMs simulate comparable leaf area indices (LAIs) with observations, while DGVM‐enabled ESMs simulate too much year‐round LAI. The variance of precipitation that is attributed to oceanic forcings in CMIP5 is comparable with observations; however, the variance of precipitation that is attributed to terrestrial forcings is smaller in CMIP5 models than observed, especially for non‐DGVM ESMs. CMIP5 models, especially those without DGVMs, undervalue precipitation's observed response strength to soil moisture anomalies. In both observations and CMIP5 models, none of the atmospheric variables show significant responses to direct vegetation forcing, except for the response in transpiration. Although vegetation has minimal direct effect on the atmospheric state, it can affect the atmosphere by modifying soil moisture and transpiration rate indirectly, which helps explain the more realistic simulation of rainfall in DGVM‐enabled ESMs than non‐DGVM ESMs. Coupling of an ESM to a DGVM is critical in generating reasonable land–atmosphere feedback and examining future ecological and climatic changes over the Sahel. [ABSTRACT FROM AUTHOR]

Human activity have caused significant increase in greenhouse gas (GHG) concentration and land use and land cover change (LULCC), which can in turn affect regional and global climate. We investigate the relative contributions of increased GHG concentration and LULCC to the precipitation and surface air temperature (SAT) in Asia through three dynamical downscaling simulations with different land cover maps and GHG concentrations. The results suggest that the increased GHG concentration leads to an increase (decrease) in precipitation over East Asia and Southeast Asia (South Asia) in spring and winter. Over Southeast Asia and the Bay of Bengal, LULCC induced a significant decrease in precipitation in autumn and winter, and the change is greater than that induced by GHG. Moreover, the enhanced GHG concentration generally leads to a decrease in dry days or light rain days and an increase in heavy rain days over the land area. However, LULCC‐induced changes in extreme precipitation are generally weak. The increased GHG concentration induces a significant increase of 1–3°C in the SAT throughout the year. LULCC plays a more important role than the increased GHG concentration in modulating the SAT over South Asia, Southeast Asia, and East Asia throughout the year. The increased GHG concentration and LULCC cause a broader range of changes in the daily temperature frequency. The anomalous warm event of 8–10°C increases by 1–1.5 days per season in the middle latitude regions in boreal spring due to the increased GHG concentration. LULCC also leads to an increase in high‐temperature events except central Asia. [ABSTRACT FROM AUTHOR]

The effect of multiyear La Niña (LN) events on precipitation in South America (SA) was assessed considering 10 persistent LN events over two successive years, referred to as Y1 and Y2 for the 1901–2012 period. Y1 spans from the austral winter of the first year to autumn of the second year, and Y2 spans from the austral winter of the second year to autumn of the third year. Comparisons were performed season by season of the Y1 and Y2. Composites revealed that the teleconnections related to a multiyear LN event during its Y1 and Y2 years, responsible for distinct seasonal precipitation anomaly patterns in SA, were associated with different tropical ocean conditions. In spring, the negative sea surface temperature (SST) dipole in the Indian Ocean during the Y2 was not observed during Y1. Different LN‐related SST anomaly patterns in the tropical Atlantic between Y1 and Y2 occurred in the other seasons. Over northern/northeastern SA, the positive precipitation anomalies became weaker (stronger) during austral summer and autumn (winter and spring) of the Y2 than Y1 and were associated with changes in the Walker cells. During austral spring and summer, southeastern presented drier conditions during the Y2 than Y1. In the spring of Y2, two Rossby wave trains, one associated with the LN‐related anomalous cooling in the equatorial Pacific and another triggered by the upper‐level anticyclone in the tropical Indian Ocean, characterized the circulation pattern over SA which explains the difference in precipitation anomalies between the Y2 and Y1. These drier (wetter) conditions during austral spring and summer (winter and spring), particularly over southern and southeastern Brazil (Colombia) in the Y2, might have more severe effects on the regional hydrological cycle than those in the Y1. The results here indicate that accurate prediction of LN duration is crucial in a climate‐monitoring context. [ABSTRACT FROM AUTHOR]

This study addresses the interannual variability and trends in weather regimes in subtropical South America and their impact over Uruguay. Weather regimes are found by applying a clustering method to the sea level pressure field in every season. We evaluate for all seasons their frequency of occurrence and transition pathways among them, their statistical relationship with global climate variability modes and their impact on precipitation and temperature in Uruguay. Overall, we find that there are five weather regimes that represent well the atmospheric dynamics over all seasons with two additional regimes in austral winter. The interannual variability in their frequency of occurrence and preferred transitions are sensitive to the phases of El Niño–Southern Oscillation and to other modes of variability. In addition, some weather regimes show positive and others negative trends in their frequency of occurrence over the last 70 years. In particular, we find that the weather regime that represents the development of a low‐pressure system over Uruguay has a negative trend (10–15% over 70 years) in all seasons except spring. [ABSTRACT FROM AUTHOR]

Tibetan Plateau vortices (TPVs) are important rainfall producers generated over the Tibetan Plateau (TP), which can move off the TP under certain conditions. The TPVs greatly affect both local and downstream rainfall, but the characteristics of TPV precipitation are not yet fully understood. Accordingly, the climatic characteristics of TPV precipitation during May–August of 1998–2018 are explored in this work from multiple aspects, based on observational TPV and precipitation data sets. Generally, maximums of precipitation amount are observed over the eastern TP and in Sichuan Basin, and the TPVs contribute more than 40% of the precipitation in certain areas within the TP. For the TPVs located over the TP, the precipitation is distributed zonally with a relatively small intensity; for the TPVs beyond the TP, the associated precipitation is distributed in the southwest–northeast direction with a larger intensity. TPVs tend to be responsible for intense rainfall, that is, heavy and torrential rainfalls, and are suggested to be the major intense rainfall producers at certain stations over and downstream from the TP. Meanwhile, TPV precipitation exhibit distinctive features in separate month, for example, TPV precipitation account for the largest percentage of the total precipitation in June and the smallest in August; more than half, even 75% of the torrential rain days downstream from the TP in Sichuan Basin and the regions around 35°N in central and eastern China in May, and in the regions over the TP and its eastern flank in June, July and August are attributed to TPVs. [ABSTRACT FROM AUTHOR]

Geographical distributions of trends in climate indices with statistical significance in Japan are investigated at each of the 51 observation stations for the recent 120 years. We employed a comprehensive set of the 70 climate indices on an annual timescale to detect climate changes and to facilitate climate information for a variety of sectors, which are developed by expert team on sector‐specific indices in the World Meteorological Organization. The rising trends have regional differences such as the annual mean of daily maximum and minimum surface air temperatures in the Kanto‐Koushin region. The statistically significant increasing trends in three daily heavy precipitation‐relevant climate indices, R95p, R99p, and RX1day, are seen in Hokuriku, Shikoku, the western part of Northern and Southern Kyusyu regions, and isolated stations in the other regions. Rotational empirical orthogonal function analysis of R99p revealed different features of statistically significant modes suggesting the trends are attributed to changes in different atmospheric phenomena as well as those of warm spell duration index. The number of the stations with statistically significant trends varies with an index. Indies relevant to daily minimum surface air temperatures have statistically significant trends at most of the 51 stations. Consecutive dry days are the most sensitive to climate changes for the 120 years among the indices relevant to precipitation in this analysis. The indices relevant to heavy precipitation are not always been most sensitive to climate change detections in Japan. The geographical distributions of the percentage of the number of indices with statistically significant changes to the total number of the stations provide information about stations sensitive to climate changes. The highest sensitivity is seen at Kumamoto. These results directly provide detection of climate changes and show the potentials of which index is sensitive to ongoing climate changes from the viewpoint of geographical distributions. [ABSTRACT FROM AUTHOR]

In this work we analyse the effects of climate change on the recharge and the groundwater flow of the Urucuia Aquifer System in western Bahia State, Brazil (UAS‐BA) in the period 2041–2060. We assessed possible changes and impacts on groundwater recharge with four climate models from the sixth phase of the Coupled Model Intercomparison Project Phase 6 (CMIP6): ACCESS‐ESM1‐5, CanESM5, EC‐Earth3 and MIROC6, under scenarios SSP1‐2.6 and SSP5‐8.5 (Shared Socioeconomic Pathways—SSP). Visual Balan 2.0 aquifer recharge assessment code, and the MODFLOW hydrogeological model were used to analyse the impact of future climate changes on the recharge. The projections of relative change in precipitation, under the SSP1‐2.6 scenario, show a precipitation reduction between 5% and 20%, using ACCESS‐ESM1‐5, CanESM5 and EC‐Earth3 models. Using SSP5‐8.5 scenario, these models behave differently, while the CanESM5 and EC‐Earth3 models indicate an even greater decrease in precipitation, with values up to 35%, the ACCESS‐ESM1‐5 projects an increase in precipitation of up to 25%. Using MIROC6, though, an increase of up to 10% in precipitation in the UAS‐BA is expected, under both scenarios. Regarding temperature, all the models agree with a warming in the future varying from 2 to 3°C. Recharge projections calculated using the Visual Balan 2.0 indicated in general a reduction between 8% and 43%. Using the MODFLOW model, the results show a reduction of recharge to the aquifer varying from 3% to 45% using CanESM5, EC‐Earth3 and MIROC6. The ACCESS‐ESM1‐5 has a 26% reduction (5% increase) under SSP1‐2.6 (SSP5‐8.5). Furthermore, projections indicate a downward trend in the water table in the northern portion of UAS‐BA, reaching a maximum drop of up to 12 m with the CanESM5, under SSP5‐8.5 scenario. [ABSTRACT FROM AUTHOR]

The Upper Blue Nile river basin (uBN) is vital to three countries' water, food, and energy security (Ethiopia, Sudan, and Egypt). It is also a region with substantial interannual precipitation variability and the potential for significant climate change in coming decades. The El Niño–Southern Oscillation (ENSO) affects both variability and trends in uBN precipitation, with El Niño episodes historically connected with below‐average rainfall. In this context, this study seeks to: (a) examine the representation of ENSO–uBN rainfall teleconnections in simulations from the sixth Coupled Model Intercomparison Project (CMIP6), as compared to the previous generation of models (CMIP5); (b) distinguish between teleconnections during the peak of the rainy season (July–August [JA]) and the agriculturally critical end‐of‐season rains (September–October [SO]); (c) investigate evidence for Tropical Easterly Jet (TEJ) mediation of these teleconnections in CMIP6 versus CMIP5 and for both peak rains and end of the rainy season. Using a subset of high performing models, we find that CMIP6 simulations of uBN precipitation show somewhat less bias than CMIP5 for JA total rainfall, and that correlations with ENSO have become more consistent across models, but that differences between CMIP5 and CMIP6 are modest. CMIP6 simulations, like CMIP5, overestimate SO rainfall and suggest a stronger ENSO association in SO than is indicated by observations. Model representation of the TEJ and its association with both ENSO and uBN precipitation show no systematic change between CMIP5 and CMIP6. The mediating influence of the TEJ appears to be more important in JA than SO. [ABSTRACT FROM AUTHOR]

The demand for meteorological gridded datasets has increased within the last few years to inform studies such those in climate, weather, and agriculture. These studies require those data to be readily usable in standard formats with continuous spatial and temporal coverage. Since 2016, Brazil has a daily gridded meteorological data set with spatial resolution of 0.25° × 0.25° from January 1, 1980 to December 31, 2013 which was well received by the community. The main objective of this work is to improve the Brazilian meteorological data set. We do this by increasing the resolution of the minimum and maximum temperature (Tmax and Tmin) gridded interpolations from 0.25° × 0.25° to 0.1° × 0.1° by incorporating data on topographic relief, and increasing the time period covered (January 1, 1961–July 31, 2020). Besides Tmax and Tmin, we also gridded precipitation (pr), solar radiation (Rs), wind speed (u2), and relative humidity (RH) using observed data from 11,473 rain gauges and 1,252 weather stations. By means of ranked cross‐validation statistics, we selected the best interpolation among inverse distance weighting and angular distance weighting methods. We determined that interpolations for Tmax and Tmin are improved by using the elevation of a query point, that accounts for topographic relief, and a temperature lapse rate. Even though this new version has ≈25 years more data relative to the previous one, statistics from cross‐validation were similar. To allow researchers to assess the performance of the interpolation relative to station data in the area, we provide two types of gridded controls. [ABSTRACT FROM AUTHOR]

The features of monsoon systems in the Northern and Southern Hemispheres are analysed in climate simulations of two atmospheric models: the Brazilian Global Atmospheric Model version 1.2 (BAM‐v1.2) and the UK Met Office Hadley Centre Global Environment Model version 3 (HadGEM3). The results are compared to GPCP precipitation and ERA5 datasets. Although they have different configurations and parameterizations, the purpose is to evaluate their ability in representing key features of the global monsoon system. The spatial extent of the monsoon domains is well simulated by the models, as well as the main characteristics of the monsoons, although precipitation biases are noticed in the regions affected by the systems, consistent with vertical motion and moisture flux biases. The largest precipitation biases are found in the West Pacific Monsoon Region, extended to the east, and in the Australia Monsoon Region extended to the Maritime continent. Deficiencies in precipitation can be related to inaccuracy of vertical motion and humidity flux, as well as to the lack of air–sea interaction. However, the atmospheric circulation features at low and high levels are well represented in all monsoon regions, as well as the annual cycle of precipitation in those regions by both models. The divergence at high levels and convergence at low levels associated with ascending air movement and precipitation in monsoon regions are well represented by the models. An analysis of two monsoon indices at eight monsoon regions showed the models are generally able to simulate the relationship between precipitation and circulation features. In the majority of years, the signs of indices from the models agree with observations. Correlations of precipitation and circulation indices between models and observations show statistically significant values for some monsoon regions. The results obtained contribute to improving knowledge about global monsoon features and their representation in the two models. [ABSTRACT FROM AUTHOR]

We 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. [ABSTRACT FROM AUTHOR]

Precipitation variability in Brazil was investigated using reanalysis data from the Global Historical Climatology Network—Monthly, version 3.01, within 1920–2010, provided freely by the University of Delaware. The analysis of space‐temporal pattern was conducted using principal component analysis (PCA) in temporal and spatial modes. It also intends to contribute with the literature that characterizes the patterns of spatiotemporal distribution of precipitation in Brazil. Temporal grouping allowed the identification of five spatial models that represents the spatial distribution of the precipitation, explained variance of 38%. Spatial grouping mode zoned through six temporal models of the areas with partially homogeneous precipitation, explained variance of 22.69%. In equatorial latitudes, the models identified Intertropical Convergence Zone (ITCZ) latitudinal and temporal variability in North and South Hemispheres. In tropical latitudes, the models evidenced anomalous precipitation due to South Atlantic Convergence Zone (SACZ) and South America Monsoon System. In subtropical latitudes, rainfall spatial anomalous patterns were associated to the di‐pole pattern between tropical and subtropical latitudes, which shows the influence of the climatic variability modes, especially El Niño‐Southern Oscillation (ENSO). Therefore, it was diagnosed that the anomalies of the atmospheric mechanisms that occur seasonally, such as SACZ and ITCZ, are the ones that cause the higher frequency in the percentage of rainfall variance, which might be explained by the frequency of the occurrence. However, remote factors, such as the occurrence in different temporal scales, interannual and interdecennial, were represented in different spatial and temporal models, as the events of variability modes vary spatially, seasonally and in intensity. It is important to highlight that the modes of climatic variability may influence the atmospheric mechanisms identified in this study, nevertheless, their impact were not evidenced in the space‐temporal scales analysed. The results are relevant to understand the space‐temporal variability of rainfall over Brazil. [ABSTRACT FROM AUTHOR]

Climate impact models often require unbiased point‐scale observations, but climate models typically provide biased simulations at the grid scale. While standard bias adjustment methods have shown to generally perform well at adjusting climate model biases, they cannot overcome the gap between grid‐box and point scale. To overcome this limitation, combined bias adjustment and stochastic downscaling methods have been developed. These methods, however, are single‐site methods and cannot represent spatial dependence. Here we propose a multisite stochastic downscaling method that can be applied to bias‐adjusted climate model output for generating spatially coherent time series of daily precipitation at multiple stations, conditional on the driving climate model. The method is based on a transformed truncated multivariate Gaussian model and can also be used to downscale to a full field at finer‐grid resolution. An evaluation for stations across selected catchments in Austria demonstrates the good performance of the stochastic model at representing marginal, temporal and spatial aspects of daily precipitation, including extreme events. [ABSTRACT FROM AUTHOR]

Northern East Asia (NEA) is characterized by wet monsoon region in the east and mid‐latitude westerly‐controlled dry inland in the west in summer, where ecosystem and environment are vulnerable to rainfall variability. Previous studies found close relationship between summer rainfall in NEA and local large‐scale atmospheric circulation systems including East Asian upper‐tropospheric westerly jet (EAJ) and middle‐tropospheric trough (EAT), and lower‐tropospheric low over NEA (NEAL). However, major features of rainfall–circulation coupling over NEA are still not fully understood. This study addresses this issue using multivariate empirical orthogonal functions method based on the ERA‐Interim reanalysis dataset and the GPCP precipitation data for 1979–2018. Three leading coupled modes are identified, together accounting for 30–50% of summer rainfall year‐to‐year variance in most of NEA. The first mode features consistent decrease of rainfall in NEA related to the decelerated EAJ, southward shifted EAT, and weakened NEAL, with a significant interdecadal change in late 1990s. The second mode exhibits a meridional dipole rainfall pattern, with less rainfall along the East Asian subtropical rainy belt and more rainfall in southern Russian Far East, coupled with the northward shifted EAJ, weakened EAT, and intensified NEAL. The third mode presents a zonal dipole rainfall pattern, with suppressed rainfall in southern Russia east of northeast China and enhanced rainfall around the Lake Baikal related to the decelerated EAJ, and westward shifted EAT and NEAL. Further investigation shows that the first mode is remotely modulated by summer rainfall anomalies in the Sahel region and Atlantic Multidecadal Oscillation (AMO) on the interdecadal timescales, and by the Polar/Eurasia teleconnection on the interannual timescales. The second and third modes are, respectively, modulated by summer rainfall in the western North Pacific and north central‐eastern India through the East Asia‐Pacific and circumglobal teleconnection patterns. [ABSTRACT FROM AUTHOR]

Seasonal precipitation forecasting is highly challenging for the northwest fringes of Europe due to complex dynamical drivers. Hybrid dynamical–statistical approaches offer potential to improve forecast skill. Here, hindcasts of mean sea level pressure (MSLP) from two dynamical systems (GloSea5 and SEAS5) are used to derive two distinct sets of indices for forecasting winter (DJF) and summer (JJA) precipitation over lead‐times of 1–4 months. These indices provide predictors of seasonal precipitation via a multiple linear regression model (MLR) and an artificial neural network (ANN) applied to four Irish rainfall regions and the Island of Ireland. Forecast skill for each model, lead time, and region was evaluated using the correlation coefficient (r) and mean absolute error (MAE), benchmarked against (a) climatology, (b) bias corrected precipitation hindcasts from both GloSea5 and SEAS5, and (c) a zero‐order forecast based on rainfall persistence. The MLR and ANN models produced skilful precipitation forecasts with leads of up to 4 months. In all tests, our hybrid method based on MSLP indices outperformed the three benchmarks (i.e., climatology, bias corrected, and persistence). With correlation coefficients ranging between 0.38 and 0.81 in winter, and between 0.24 and 0.78 in summer, the ANN model outperformed MLR in both seasons in most regions and lead‐times. Forecast skill for summer was comparable to that in winter and for some regions/lead times even superior. Our results also show that climatology and persistence performed better than direct use of bias corrected dynamical outputs in most regions and lead‐times in terms of MAE. We conclude that the hybrid dynamical–statistical approach developed here—by leveraging useful information about MSLP from dynamical systems—enables more skilful seasonal precipitation forecasts for Ireland, and possibly other locations in western Europe, in both winter and summer. [ABSTRACT FROM AUTHOR]

The atmospheric water cycle over the Hengduan Mountains (HM) is crucial to the local and downstream ecosystem and social economy, while its characteristics and variability have not been thoroughly revealed yet. Based on a bulk method, the atmospheric water cycle over the HM and its difference between wet and dry summers is analysed in this study. Results show that the summer precipitation recycling ratio over the HM is 11.5% in climatology and is significantly and negatively correlated with precipitation there on interannual timescale. In wet summers, the precipitation recycling ratio decreases by 13% compared to dry summers. The origin of water vapour for precipitation over the HM is mainly contributed by the external water vapour transport (local evaporation) in wet (dry) summers. August is found to have the strongest climatological mean precipitation recycling ratio (15.2%) and atmospheric water cycle among 3 months in summer. Also in August, the difference in precipitation recycling ratio between wet and dry summers is largest. In wet summers, although the increase in the external moisture inflow is the largest in June (3.0 × 107 kg·s−1), the increase of precipitation is the largest in August (2.6 × 107 kg·s−1) due to its stronger atmospheric water cycle. Our study promotes the knowledge of the atmospheric water cycle over the HM and provides a new perspective to understand the interannual variations of the precipitation over the HM. [ABSTRACT FROM AUTHOR]

The relationship between the spring precipitation in the middle and lower reaches of the Yangtze River valley and the North Atlantic Oscillation in the preceding winter is investigated based on data diagnosis. Through affecting the dynamics and thermodynamics in near‐surface atmosphere over Eurasia, the North Atlantic Oscillation in January can exert significant influence on the Eurasian snow cover to form a dipole pattern of snow anomalies. The dipole pattern of snow anomalies stimulates the Rossby wave train in the middle and high latitudes of the Northern Hemisphere in the following spring. Two activity centres associated with the wave train, one in the middle latitudes of Northwest Pacific with an anomalous anticyclone circulation and another in Mongolia with an anomalous cyclone circulation, bring the water vapour from the south and converge over the middle and lower reaches of the Yangtze River, resulting in the excessive precipitation over there. The results indicate that the North Atlantic Oscillation in January can act as a precursor signal for predicting the spring rainfall anomalies in the middle and lower reaches of the Yangtze River. [ABSTRACT FROM AUTHOR]

Reliable long‐term observations from precipitation stations are often required for climatological studies but are strongly limited in many regions of the world. To improve this limitation for West Africa, we compiled daily and monthly observations from more than 20 national, continental and global databases, to establish a historical precipitation archive with a focus on four countries (Burkina Faso, Ghana, Benin and Togo). The new archive contains long‐term daily and monthly precipitation measurements from 1819 to 2013 for more than 1,000 sites. It is, therefore, the most comprehensive historical dataset with daily and monthly precipitation observations for this region. To produce a quality‐controlled and harmonized precipitation dataset for the focal region, various statistical algorithms have been implemented. These algorithms rely on straightforward geostatistical approaches (e.g., spatial correlograms) and corresponding statistical tests for identification and elimination of unreliable time series, in addition to various standard approaches used by global data centers. Although the quality control revealed various data errors and uncertainties for measurements and meta‐information (e.g., unit conversion errors, temporal offsets, frequent and long data gaps), a spatial interpolation using the quality‐controlled and harmonized dataset produced relatively reliable precipitation patterns for different target variables (e.g., monthly precipitation amount and daily precipitation probability). A major remaining challenge is providing free access to this database for research and other noncommercial purposes, due to national data protection regulations. However, several further tasks have been initiated and implemented (e.g., free provision of gridded precipitation datasets and point statistics) to improve the access and availability of station‐based precipitation observations and related data products for this climatologically challenging region. [ABSTRACT FROM AUTHOR]

This study examined the influence of atmospheric blocking on the variability of precipitation over South Korea during summer (June–July–August) by defining the blocking frequency over the Okhotsk Sea (Okhotsk Sea blocking frequency; OK_BF). According to composite analysis for the years of high precipitation over South Korea, blocking occurs more frequently over the Okhotsk Sea (140(E–160(E). Partial correlation and regression analyses were conducted to separate the contribution of OK_BF to precipitation variability from that of the low-level meridional wind (LLMW) because LLMW over the region is another important aspect of summer precipitation in South Korea. The barotropic structures of positive geopotential height anomalies over the Okhotsk Sea associated with increasing OK_BF can induce negative temperature anomalies over South Korea due mainly to the equatorward advection of cold air masses from higher latitudes. The enhanced meridional temperature gradient can cause increases in baroclinic instability and zonal wind vertical shear according to the thermal wind balance. This instability can induce anomalous cyclonic circulations over South Korea, resulting in positive precipitation anomalies. The partial correlation coefficients (R2 = 0.35–0.40) between the OK_BF and precipitation indices, including mean precipitation, extreme precipitation intensity, wet days, and consecutive wet days, were all statistically significant at the 95% confidence level. Overall, the effects of the increasing OK_BF on both precipitation and potential evapotranspiration can intensify the surface water budget in South Korea. [ABSTRACT FROM AUTHOR]

The present study assessed the spatiotemporal variation of summer monsoon precipitation and its potential drivers in Myanmar, utilizing monthly precipitation data from forty-six (46) synoptic meteorological stations spanning 1981–2020. The nonparametric statistical Mann–Kendall (MK), Sequential Mann–Kendall (SQMK) test, Empirical Orthogonal Function (EOF), and Probability Distribution Function (PDF) were used to determine the spatiotemporal monsoon precipitation trends and variability over the study period. The results show that higher precipitation occurs during June, July and August (peak monsoon period), while low precipitation was detected in May (onset month), September and October (withdrawal monsoon period), respectively. Moreover, abrupt change in precipitation is observed after 1990 with a significant (95% confidence level) increasing trend from 2000 to 2020. Decadal precipitation experienced the highest fluctuation during 2011–2020, a positive shift and increased frequency in recent decades. The spatial trends for monthly and seasonal precipitation vary from station to station and region to region due to a fluctuated shift of climatic dynamics. During dry conditions, less cloud liquid water suppressed relative humidity and high air temperature were exhibited, thus implying less precipitation in the region. However, the wet years revealed strong moisture/water vapour into the inland regions from the ocean, increased relative humidity, and suppressed air temperature. In addition, no significant relationship was found between El-Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and monsoon onset timing with precipitation variability over Myanmar. This study provides essential information on manageable climate adaptation, mitigation and weather forecasting strategies in Myanmar. [ABSTRACT FROM AUTHOR]

The Philippines is exposed to tropical cyclones (TCs) throughout the year due to its location in the western North Pacific. While these TCs provide muchneeded precipitation for the country's hydrological cycle, extreme precipitation from TCs may also cause damaging hazards such as floods and landslides. This study examines the relationship between TC extreme precipitation and TC characteristics, including movement speed, intensity and season, for westward-moving TCs crossing Luzon, northern Philippines. We measure extreme precipitation by the weighted precipitation exceedance (WPE), calculated against a 95th percentile threshold, which considers both the magnitude and spatial extent of TC-related extreme precipitation. WPE has a significant, moderate positive relationship with TC intensity with a non-significant, weak negative relationship with movement speed. When TCs are classified by intensity 1 day before landfall (or pre-landfall), Typhoons (1-min maximum sustained wind speed ≥64 knots) tend to yield higher WPE than non-Typhoons (<64 knots). On the other hand, when TCs are classified by pre-landfall speed, slow TCs (movement speed <11.38 knots) tend to yield higher WPE than fast TCs (movement speed ≥11.38 knots). However, the relationship between prelandfall TC intensity and WPE is more pronounced during June–September while there is no significant difference between the WPE of the southwest monsoon (June–September) and northeast monsoon (October–December) seasons. These results suggest that it is important to consider the pre-landfall cyclone movement speed, intensity and season to anticipate extreme precipitation of incoming TCs. A decision table considering these factors is devised to aid in TC extreme precipitation forecasting. [ABSTRACT FROM AUTHOR]

Sudan is one of the most vulnerable countries to climate change in the African Sahel region. Given the urgent need to understand the potential behaviour of the future climate, this study assessed the skill of 32 global climate models (GCMs) from the fifth (CMIP5) and the sixth (CMIP6) phases of the Coupled Model Intercomparison Project (CMIP) in simulating summer monsoon precipitation over Sudan for the period 1946–2005. The models were ranked based on the Skills Score, estimated using three statistical metrics, correlation coefficient, index of agreement, and normalized root mean square error. The best performing models and their ensembles were used for long‐term trend quantification, empirical orthogonal function (EOF) analysis, and assessment of precipitation associations with atmospheric circulation. GISS‐E2‐H, IPSL‐CM5A‐MR, and MPI‐ESM‐LR of CMIP5 and BCC‐CSM2‐MR, INM‐CM4‐8, and MPI‐ESM1‐2‐LR of CMIP6 showed the best performance in Sudan. Instead of August's unimodal peak rainfall, the GCMs, especially the CMIP5 GCMs, showed an unrealistic rainfall peak in May and a shift of the August rainfall peak to September. Only the CMIP6 multi‐model ensemble reliably reproduced the unimodal rainfall property. All models of both phases failed to reconstruct the dominant mode of monsoon precipitation as determined using EOF analysis, particularly in the country's southern parts. The models also failed to reproduce the long‐term rainfall trends in different climate zones. Only MPI‐ESM‐LR (CMIP5) and MPI‐ESM1‐2‐LR (CMIP6) were successful in simulating monsoon rainfall associations with sea surface temperature, El Niño‐Southern Oscillation teleconnection, and winds components at 850 hPa. [ABSTRACT FROM AUTHOR]

In this study, the projected changes of the average monthly and annual air temperatures, precipitation amounts and De Martonne aridity index in Serbia in the 21st century are examined. For that purpose, the spatial distribution of air temperature and precipitation amount is statistically modelled using data of 64 climatologic stations and 421 precipitation stations for the period 1976–2005 using multiple linear regression. The De Martonne aridity index is calculated from the spatially distributed data. In order to obtain regional averages and variability, we divide Serbia into three subdomains: the western zone (the Dinarides), eastern mountainous (Carpathian‐Balkan) zone and the terrains with altitudes lower than 500 m a.s.l. (Lowland). As a second step, the members of the COordinated Regional Downscaling EXperiment (CORDEX) for Europe (EURO‐CORDEX) are evaluated in terms of near‐surface air temperature and precipitation for Serbia. By comparing respective values for the period 1976–2005 with future periods 2021–2050 and 2071–2100, we derive the climate change signal for near‐surface air temperature, precipitation and the De Martonne index for both scenarios RCP4.5 and RCP8.5. Hereby, the ensemble averages are calculated as weighted averages using the inverse standardized Euclidean distance by comparing observation and model results for near‐surface air temperature and precipitation as a weight. We find for the period 2071–2050 that most ensemble members predict a statistically significant increase in monthly and annual near‐surface air temperature in both scenarios in all three subdomains, and a statistically significant decrease in the yearly De Martonne index in the RCP8.5 scenario in the Carpathian‐Balkan and Dinarides. For the last decades of the 21st century, our findings indicate much higher temperatures and increased risks of drought for the RCP 8.5 scenario compared to both the observations from 1976 to 2005 and the RCP 4.5 scenario. [ABSTRACT FROM AUTHOR]

Global change affects precipitation and small modifications in rainfall regimes could compromise water availability, especially in areas characterized by a strong seasonality and a climate ranging from dry sub‐humid to semi‐arid. Drought, in particular, is a creeping phenomenon defined as an important temporary decrease in water availability below the expected value in a certain period and a specific area. Among the different methodologies for analysing drought periods, one of the most used is the evaluation of dry spells. The aim of this study is to investigate spatial and temporal changes in dry spells in Tuscany during the period 1955–2017, by using daily precipitation data. Changes in the number of dry days, maximum duration and 75th percentile of the duration of the dry spells were investigated. Three precipitation thresholds were selected: 1, 5 and 10 mm. The analyses were carried out at annual, September–May, September–November and December–May level. Theil–Sen nonparametric method was used to estimate the slope; Mann–Kendall test and a modification of it, which applies a trend‐free pre‐whitening (TFPW) method to the series having a serial correlation, were applied to look for statistically significant trends. The drought indicators showed increasing trends (some significant) especially at December–May and to a lesser extent at the annual and September–May level; the most pronounced increases were found in the southernmost areas of Tuscany. During September–November period, on the other hand, a prevalence of decreasing trends (rarely significant), with less pronounced decreases in the south of the region, were observed. Mediterranean area is considered a climate change "hot‐spot" from the viewpoint of vulnerability. An increase in long‐lasting dry spells, especially in southern Tuscany, can exacerbate the problems linked to water management and demand. [ABSTRACT FROM AUTHOR]

Ascension Island has had a long intermittent record of instrumental weather recording. Here, we develop monthly records of mean temperature and precipitation totals (for 1924–2020 when an almost complete record is available) from the three principal recording sites: the capital Georgetown and two gauges at Wideawake Airfield. Although some of the data are in global climate databases, we have sourced as much data as possible from the primary sources in the United Kingdom, the United States and from the island itself. Air temperature shows statistically significant warming since 1950 of 0.54°C and since 1979 of 0.40°C, and agrees closely with sea surface temperatures, taken from the seas around the island, back to the start of the island series in 1924. Although the island is too small to be in Reanalyses, the warming trends of air temperatures from these products also agree, but the absolute air temperature values are about 1°C cooler than measured on the island. Annual precipitation on the island indicates it is very arid, with a long‐term average of only 165 mm. Occasionally, heavy monthly precipitation totals occur (always between February and June) which bring severe damage to the island's infrastructure and ecosystems. The heaviest monthly total was 334 mm for April 1985. [ABSTRACT FROM AUTHOR]

Smallholder livelihoods throughout Central America are built on rain‐fed agriculture and depend on seasonal variations in temperature and precipitation. Recent climatic shifts in this highly diverse region are not well understood due to sparse observations, and as the skill of global climate products have not been thoroughly evaluated. We examine the performance for several reanalysis and satellite‐based global climate data products (CHIRPS/CHIRTS, ERA5, MERRA‐2, PERSIANN‐CDR) as compared to the observation‐based GPCC precipitation dataset. These datasets are then used to evaluate the magnitude and spatial extent of hydroclimatic shifts and changes in aridity and drought over the last four decades. We focus on water‐limited regions that are important for rain‐fed agriculture and particularly vulnerable to further drying, and newly delineate those regions for Central America and Mexico by adapting prior definitions of the Central American Dry Corridor. Our results indicate that the CHIRPS dataset exhibits the greatest skill for the study area. A general warming of 0.2–0.8°C·decade−1 was found across the region, particularly for spring and winter, while widespread drying was indicated by several measures for the summer growing season. Changes in annual precipitation have been inconsistent, but show declines of 20–25% in eastern Honduras/Nicaragua and in several parts of Mexico. Some regions most vulnerable to drying have been subject to statistically significant trends towards summer drying, increases in drought and aridity driven by precipitation declines, and/or a lengthening of the winter dry season, highlighting areas where climate adaptation measures may be most urgent. [ABSTRACT FROM AUTHOR]

Bias‐corrected monthly precipitation and surface temperature datasets are constructed for the monsoon transitional zone (MTZ) in China during 1965–2100 based on 21 coupled climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) using the equi‐ratio and equidistant cumulative distribution function quantile‐based mapping methods (equi‐ratio cumulative distribution function [ERCDF] and equidistant cumulative distribution function [EDCDF]), respectively. The efficiencies of the two methods are verified via cross‐validation by the jack‐knife method, and the biases are remarkably reduced compared to those of the raw model outputs. Then, the bias‐corrected model outputs are applied to future projections of precipitation, surface temperatures and drought events under the medium (SSP2‐4.5) and high (SSP5‐8.5) shared socioeconomic pathway scenarios. The obtained results present pronounced increases in both the projected annual mean precipitation and temperature fields for the model ensemble mean. In accompany, the MTZ is predicted to become drier in the three future periods; this result was attributed to the dramatic increases in semiarid events and to the decreases in humid events. The drought tension showed more rapid development under SSP585 than under the other scenario, with comparable occurrence frequencies of relatively wet and dry events in the long‐term period, posing a serious threat to regional sustainable development. [ABSTRACT FROM AUTHOR]