Consecutive Dry Days Research Articles

Analysis of past and projected changes in extreme precipitation indices in some watersheds in Côte d'Ivoire

Abstract The purpose of this study is to analyse trends in annual rainfall extremes over five watersheds within Côte d'Ivoire using observed (1976–2017) and projected (2020–2050) rainfall data from the fourth version of the Rossby Centre regional atmospheric model, RCA4, for the representative concentration pathways RCP 4.5 and RCP 8.5. Four rainfall extreme indices, namely, the consecutive dry days (CDD), maximum annual rainfall (Pmaxan), very wet day (R95p), and maximum 5-day rainfall (Rx5days), were considered for trend analysis by using the non-parametric modified Mann–Kendall test and the distribution mapping bias-correction technique to adjust the simulated regional climate model of the simulated daily precipitation. As a result, it is found that during the period 1976–2017, there was a significant downward trend in the drought-related index (CDD) at the Bagoue, Baya, Agneby, and Lobo watersheds. The Baya and N'zo watersheds also experienced a significant downward trend under the RCP 4.5 and RCP 8.5 scenarios. The flood-related indices (Pmaxan, R95p, and Rx5days) show a clear downward trend in the recorded data for almost all the considered watersheds and generally a significant upward trend for both cases. These findings indicate that the watersheds are vulnerable to climate-induced disasters.

Factors Affecting the Spatiotemporal Variation of Precipitation in the Songhua River Basin of China

The study aimed to investigate the spatiotemporal variation of annual precipitation and extreme precipitation within the Songhua River Basin (SRB). It utilized precipitation data collected from 60 meteorological stations within the SRB during the period 1968–2019. Employing Empirical Orthogonal Function (EOF) analysis, it decomposed spatiotemporal characteristics of annual precipitation in the SRB. Through Pearson correlation analysis, application of the cross-wavelet transform, and wavelet coherence analysis, the current study explored the correlation between geographical factors, local air temperature, circulation factors, and annual and extreme precipitation. The results indicated an increasing trend for annual precipitation and for most indices of extreme precipitation within the SRB, apart from the consecutive dry days (CDD). Spatially, a general pattern of “more in the east and less in the west” was observed. Annual precipitation types in the basin were resolved into two modes with the first mode showing a general tendency of more (or less) precipitation over the entire basin, while the second mode exhibited less (or more) precipitation in the western areas and more (or less) in the eastern areas. Longitude, latitude, and altitude significantly impacted annual precipitation and extreme precipitation. Local air temperature notably affected the consecutive wet days (CWD). The West Pacific Subtropical High (WPSH) exerts a strong influence on the annual precipitation and extreme precipitation within the basin.

Evaluation of statistical downscaling techniques and projection of climate extremes in central Texas, USA

This study evaluates statistical downscaling techniques using different metrics and compares climate change signals and extreme precipitation and temperature changes under future climate change scenarios in the Bosque watershed, North-Central Texas. The study utilizes observed gridded Daymet data to assess the effectiveness of statistical downscaling techniques. It involves comparing the mean, the 90th percentile, 10th percentile, wet day frequency, and Cumulative Distribution Function (CDF) of climate model simulations before and after downscaling and the Daymet data during the historical period (1981–2005). Furthermore, the study analyzes changes in climate change signals, extreme precipitation, and temperature values under both near-future (2031–2060) and far-future (2070–2099) climate scenarios. The Ratio Delta method (DeltaSD) and Equi-Distant Quantile Mapping (EDQM) statistical downscaling techniques adjust the mean annual, the wet days frequency, the 90th and 10th percentiles, and the CDF of Global Climate Models (GCMs) simulations of historical precipitation and temperature. The downscaling techniques influenced the climate change signal and changes in extreme values in the future climate. When examining future climate projections produced using the DeltaSD method, we observe a more pronounced reduction in precipitation, while simulations generated through EDQM exhibit a higher frequency of heavy precipitation events (R10mm, R20mm) and consecutive dry days (CDD). It's worth noting that the uncertainties associated with the statistical downscaling techniques are relatively small and not statistically significant (≤0.05). In contrast, substantial and significant uncertainties arise from the choice of emission scenarios and the selection of driving GCMs. Across most climate change scenarios, there is a consistent trend towards increased temperatures and extreme temperature indices. The trend of extreme temperature indices shows variation following the choice of emission scenarios where a significant change in temperature extremes was detected under the RCP8.5 emission scenario.

Future projections of extreme rainfall events in Indonesia

AbstractPrevious evaluations of coupled climate models have indicated that under the influence of global warming precipitation extremes, including those over Indonesia, are expected to intensify. Here, we examine the most recent future projections of extreme rainfall in Indonesia using 24 global climate models from the Coupled Model Intercomparison Project Phase 6, consisting of 11 low resolution (LR) and 13 medium resolution (MR) models. The performance of these models is evaluated against Climate Hazards Group InfraRed Precipitation with Station data observations and realistically reproduce Indonesia's mean climatology over the period assessed (1987–2014). Overall, both LR and MR multi‐model ensemble means (MMEM) outperform individual models. Interestingly, extreme rainfall projections vary across different seasons, time periods, spatial resolutions and climate change scenarios. The LR and MR MMEMs project a continuous increase in wet extremes (R95p and Rx5d) during the wet season across most of Indonesia over the 21st century. Conversely, dry extremes (consecutive dry days [CDD]) are projected to increase (decrease) over the south (north) during the wet season but increase countrywide during the dry season. We show that future extreme wet and dry events are projected to be more frequent and intense, with upper extreme values surpassing historical records. The MR models project smaller changes in extreme wet indices than the LR models but simulate a more prolonged extreme dry index. Key findings here indicate that forthcoming instances of extreme rainfall present substantial hazards, necessitating the implementation of adequate preventative measures by policymakers, particularly within densely inhabited regions, such as Java.

The Effect of the El Nino Southern Oscillation on Precipitation Extremes in the Hindu Kush Mountains Range

The El Nino Southern Oscillation (ENSO) phenomenon is devastating as it negatively impacts global climatic conditions, which can cause extreme events, including floods and droughts, which are harmful to the region’s economy. Pakistan is also considered one of the climate change hotspot regions in the world. Therefore, the present study investigates the effect of the ENSO on extreme precipitation events across the Upper Indus Basin. We examined the connections between 11 extreme precipitation indices (EPIs) and two ENSO indicators, the Southern Oscillation Index (SOI) and the Oceanic Niño Index (ONI). This analysis covers both annual and seasonal scales and spans the period from 1971 to 2019. Statistical tests (i.e., Mann–Kendall (MK) and Innovative Trend Analysis (ITA)) were used to observe the variations in the EPIs. The results revealed that the number of Consecutive Dry Days (CDDs) is increasing more than Consecutive Wet Days (CWDs); overall, the EPIs exhibited increasing trends, except for the Rx1 (max. 1-day precipitation) and Rx5 (max. 5-day precipitation) indices. The ENSO indicator ONI is a temperature-related ENSO index. The results further showed that the CDD value has a significant positive correlation with the SOI for most of the UIB (Upper Indus Basin) region, whereas for the CWD value, high elevated stations gave a positive relationship. A significant negative relationship was observed for the lower portion of the UIB. The Rx1 and Rx5 indices were observed to have a negative relationship with the SOI, indicating that El Nino causes heavy rainfall. The R95p (very wet days) and R99p (extreme wet days) indices were observed to have significant negative trends in most of the UIB. In contrast, high elevated stations depicted a significant positive relationship that indicates they are affected by La Nina conditions. The PRCPTOT index exhibited a negative relationship with the SOI, revealing that the El Nino phase causes wet conditions in the UIB. The ONI gave a significant positive relationship for the UIB region, reinforcing the idea that both indices exhibit more precipitation during El Nino. The above observations imply that while policies are being developed to cope with climate change impacts, the effects of the ENSO should also be considered.

Precipitation Projection in Cambodia Using Statistically Downscaled CMIP6 Models

The consequences of climate change are arising in the form of many types of natural disasters, such as flooding, drought, and tropical cyclones. Responding to climate change is a long horizontal run action that requires adaptation and mitigation strategies. Hence, future climate information is essential for developing effective strategies. This study explored the applicability of a statistical downscaling method, Bias-Corrected Spatial Disaggregation (BCSD), in downscaling climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and then applied the downscaled data to project the future condition of precipitation pattern and extreme events in Cambodia. We calculated four climate change indicators, namely mean precipitation changes, consecutive dry days (CDD), consecutive wet days (CWD), and maximum one-day precipitation (rx1day) under two shared socioeconomic pathways (SSPs) scenarios, which are SSP245 and SSP585. The results indicated the satisfactory performance of the BCSD method in capturing the spatial feature of orographic precipitation in Cambodia. The analysis of downscaled CMIP6 models shows that the mean precipitation in Cambodia increases during the wet season and slightly decreases in the dry season, and thus, there is a slight increase in annual rainfall. The projection of extreme climate indices shows that the CDD would likely increase under both climate change scenarios, indicating the potential threat of dry spells or drought events in Cambodia. In addition, CWD would likely increase under the SSP245 scenario and strongly decrease in the eastern part of the country under the SSP585 scenario, which inferred that the wet spell would have happened under the moderate scenario of climate change, but it would be the opposite under the SSP585 scenario. Moreover, rx1day would likely increase over most parts of Cambodia, especially under the SSP585 scenario at the end of the century. This can be inferred as a potential threat to extreme rainfall triggering flood events in the country due to climate change.

Evaluation of present-day extreme precipitation over the United States: an inter-comparison of convection and dynamic permitting configurations of E3SMv1

Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models, which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ∼25 km) and multiscale modeling framework (MMF, i.e. replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (i.e. low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (climate prediction center daily US precipitation and integrated multi-satellite retrievals for global precipitation measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the expert team on climate change detection and indices. Our results show that both the dry (i.e. consecutive dry days (CDD)) and wet (i.e. consecutive wet days, maximum 5 day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many CDD during the summer, contributing to a low mean precipitation bias.

Climate Change and Extreme Events in Northeast Atlantic and Azores Islands Region

In small island regions, the influence of climate change assumes particular relevance. In the Azores archipelago, made up of nine islands, the geographical circumstances, oceanic condition, territorial dispersion, land use model and other physiographic constraints reinforce and enhance the vulnerability of the islands to changes in current weather patterns. Coupled Model Intercomparison Phase 6 (CMIP6) projections are used for the northeast Atlantic region to evaluate daily extreme climate events in large scale for the Azores region. Results shows changes in the annual maximum number of consecutive dry days, the annual number of wet days, and especially in the annual number of tropical nights. Despite limitations due to the lack of spatial detail, the large-scale framework suggests changes that may be enhanced by topography, particularly with respect to precipitation. The conclusions point to the need to establish standard rules in the processes of design, reviewing and/or amending territorial management instruments at the municipal scale in the Autonomous Region of the Azores, with the goal of adapting to a different climate from the recent past.

Analysis of river drying in Slovenia

Intermittent watercourses lack water during some periods of the year, which especially applies to karst sinking streams. Droughts also occur in dry periods with no rainfall over a longer period of time. The drying up of Slovenian rivers was analysed using the data from the state hydrological monitoring of the Slovenian Environment Agency. By analysing the periodic statistics of minimum low discharges (nQnp), 18 gauging stations were extracted where the watercourses dried up during the measurement period. For these stations, sets of mean daily discharges were analysed, and dry days where the flow was equal to zero were listed. The number of dry days and the number of consecutive dry days were analysed by month and year, and the longest dry periods were determined. The results are presented graphically. At most of the gauging stations considered, the number of dry days was the highest in the years characterized by extreme hydrological droughts, namely 2003, 2012, and 1993. In some watercourses, drying up is a constant phenomenon that occurs almost every year, while others only dry up in years characterized by extreme droughts. Exceptions to this are the karst rivers, which sink and are therefore dry for part of the year. The Pivka and Branica Rivers regularly dry up. Watercourses in Pomurje and Velunja, Kožbanjšček, and Dragonja also dry up very often. Analysis results demonstrated that river drying has become more frequent in the past two decades. The driest month is August, followed by July and September.

Spatiotemporal analysis of transition probabilities of wet and dry days under SSPs scenarios in the semi-arid Susurluk Basin, Türkiye

Precipitation, especially in regions dominated by the Mediterranean climate, is one of the most critical parameters of the hydrological cycle and the environment affected by climate change. One the one hand, the transition probabilities of wet and dry days in precipitation occurrence are a relatively new topic, on the other hand these are essential in defining the regional climate. For the first time, spatiotemporal variations of transition probabilities of wet and dry days in the Susurluk Basin, northwestern Türkiye, dominated by a semi-arid Mediterranean climate and also having a mountain climate, were analyzed based on the observation (1979–2014) and future terms (2030–2059 as short and 2070–2099 as long), under four Shared Socioeconomic Pathways (SSPs) scenarios. To do this, statistical downscaling was performed for 14 general circulation models (GCMs) from the CMIP6. By applying an ensemble of four high-performing GCMs, four indices for transition probabilities of wet and dry, i.e., a dry day following a dry day (FDD), a wet day following a dry day (FDW), a dry day following a wet day (FWD), and a wet day following a wet day (FWW), were calculated, and their changes were determined statistically. Monotonic and partial trends of the indices were also analyzed. According to the results, the FDD will increase in water year and wet period and autumn in the future, especially for the long term, in the basin dominated by the FDD (75 % in water year). The risks are higher in the western part of the basin, where human activities are intense, as the FDD is higher in this part than other parts especially in summer (90–100 %) in SSP3-7.0 and SSP5-8.5 scenarios for the long term. So, the length of consecutive dry days in the wet period and water year will increase in the basin, thus increasing the likelihood of droughts. As for the intra-term trends, the FDD increases and the FWW decreases in the water year and seasons in SSP3-7.0 and SSP5-8.5, contrary to the observation term.

Impacts of changes in climate extremes on wildfire occurrences in China

Climate change has caused more frequent instances of extreme climatic events around the world, being an influential factor on the occurrence of wildfires in China on large scale. However, the impact of changes in extreme climate on the occurrence of wildfires in different climate zones remains unclear. In the present study, 26 extreme climate indices were selected to analyze the thereof relationship with wildfire occurrences from 2005 to 2018 in different regions of China. Wildfires in China primarily occur in the south, with a measurable presence in the north. On an annual scale, the wildfire occurrences in southwestern China show stronger correlations with mean temperature than extreme temperature indices, but show stronger correlations with extreme precipitation indices than the total precipitation. On the contrary, the wildfire occurrences in southeastern China show stronger correlations with the total precipitation than extreme precipitation indices, but show stronger correlations with extreme temperature indices than the mean temperature. In Northeast China, wildfires show a more significant correlation with mean temperature than with any extreme climate indices, indicating a minimal impact from extreme climatic conditions. The fire-climate relationships in the main fire season (January-April) are similar to those in the annual scale. The wildfire occurrences in the southwestern, south-central, and southeastern China, which are located in the same latitudes, were affected by extreme climate indices of different types and on different time scales. Furthermore, we recommend that consecutive dry days (CDD) and diurnal temperature range (DTR) should be considered first when studying the relationship between wildfire occurrence and extreme climate in southwestern and southeastern China respectively.

Precipitation reconstructions for Paris based on the observations by Louis Morin, 1665–1713 CE

Abstract. This paper presents a precipitation reconstruction that is based on the continuous observations by Louis Morin in Paris from 1665–1713. Morin usually recorded precipitation intensity and duration three times each day (sometimes up to six times) when it snowed or rained. The continuity of his observations can be calculated considering all measurements and observations (e.g., temperature, cloud cover), where on 98.7 % of all days between February 1665 and July 1713 at least one entry per day is noted. To convert these observations to common units, we calibrated them with a multiplicative interacting model using Philippe and Gabriele-Philippe de la Hire's instrumental measurements from Paris. The two series of measurements by de la Hire (father and son) and observations by Morin overlap from 1688–1713. To test the quality of the reconstruction, we analyzed it with the de la Hire's measurements, proxy data, an internal analysis of Morin's measurements of different climate variables, and modern data. Thus, we assess the reliability of the precipitation reconstructions based on Morin's data as follows. We have moderate confidence regarding the exact quantities of daily, seasonal, and annual precipitation totals. We have low confidence regarding exceptionally high precipitation amounts, but we have high confidence in the indices of an impact analysis (i.e., dry days, wet days, consecutive dry days, consecutive wet days); in monthly frequencies of rainfall; and in interannual, interseasonal, and interdecadal variability. Rainy seasons with precipitation totals greater than 250 mm occurred in MAM 1682, JJA 1682, SON 1687, JJA 1697, and JJA 1703. Furthermore, compared to other DJF seasons, the winter of 1666/1667 slightly stands out with a precipitation total of 214.6 mm. Dry seasons with precipitation totals less than 60 mm occurred in SON 1669, DJF 1671/1672, and DJF 1690/1691. An impact analysis shows no abnormalities regarding consecutive dry days or wet days in MAM. In JJA a longer dry period of 31 days appeared in 1686 and a dry period of 69 d appeared in DJF 1671/1672.

Future changes in mean and extreme precipitation over Peninsular Malaysia using CORDEX-SEA 5 km simulations

The Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia further downscaled three of its 25 km products, i.e. EC-Earth, HadGEM2-ES and MPI-ESM-MR forced regional climate simulations to a higher resolution of 5 km. A newer RegCM4.7 model was used for this exercise. For the simulations over Peninsular Malaysia, analysis shows that these simulations have much smaller precipitation biases. They can correctly predict the annual precipitation cycle over the east coast of Peninsular Malaysia compared to the 25 km simulations. The ensemble of these 5 km simulations indicates that Peninsular Malaysia is expected to experience a decrease in its seasonal mean precipitation, regardless of the RCP 4.5 or RCP 8.5 scenarios. Aside from the reduction of seasonal mean precipitation, consecutive dry and wet days are expected to increase and decrease, respectively. This indicates that Peninsular Malaysia will experience a long dry spell in the future. At the same time, it is suggested that Peninsular Malaysia will have fewer days with very heavy precipitation. Overall, the findings from this study suggest that the 5 km downscaled climate simulations improve significantly over 25 km and that the Peninsular Malaysia region can expect a drier future climate and extremes.

Two Large-Scale Meteorological Patterns are Associated with Short-Duration Dry Spells in the Northeastern United States

Abstract Large-scale meteorological pattern (LSMP)–based analysis is used in a novel way to understand meteorological conditions before and during short-duration dry spells over the northeastern United States. These LSMPs are useful to assess models and select better-performing models for future projections. Dry-spell events are identified from histograms of consecutive dry days below a daily precipitation threshold. Events lasting 12 days or longer, which correspond to ∼10% of dry-spell events, are examined. The 500-hPa streamfunction anomaly fields for the first 12 days of each event are time averaged, and k-means clustering is applied to isolate the dry-spell-related LSMPs. The first cluster has a strong low pressure anomaly over the Atlantic Ocean, southeast of the region, and is more common in winter and spring. The second cluster has strong high pressure over east-central North America and is most common during autumn. Over the region, both clusters have negative specific humidity anomalies, negative integrated vapor transport from the north, and subsidence associated with a midlatitude jet stream dipole structure that reinforces upper-level convergence. Subsidence is supported by cold-air advection in the first cluster and the location on the east side of the lower-level high pressure in the second cluster. Extratropical cyclone storm tracks are generally shifted southward of the region during the dry spells. Individual events lie on a continuum between two distinct clusters. These clusters have similar local, but different remote, properties. Although dry spells occur with greater frequency during drought months, most dry spells occur during nondrought months. Significance Statement This study examines the large-scale weather patterns and meteorological conditions associated with dry-spell events lasting at least 2 weeks while affecting the northeastern United States. A statistical approach groups events together on the basis of similar atmospheric features. We find two distinct sets of patterns that we call large-scale meteorological patterns. These patterns reduce moisture, foster localized sinking, and shift the storm track southward along the Atlantic seaboard, all of which reduce precipitation. Besides greater understanding, knowing the meteorological patterns during short-term dryness in the region provides an important tool to assess how well atmospheric models reproduce these specific patterns. More dry spells occur in nondrought months than in drought months, which means that dry spells can occur without preexisting drought conditions.

A Statistical Approach on Estimations of Climate Change Indices by Monthly Instead of Daily Data

Climate change indices (CCI) profoundly contribute to understanding the climate and its change. They are used to present climate change in an easy-to-understand and tangible way, thus, facilitating climate communication. Most of these indices are calculated by daily data but there are also many valuable data sets that consist solely of a monthly temporal frequency. In this paper, we present a method that enables the estimation of specific CCIs from monthly instead of daily data, allowing the expression and examination of data sets consisting solely of monthly parameters through climate change indices. Therefore, we used the ERA5 Land data supplemented by a CMIP6 ssp5-8.5 climate projection to train multiple regression models with different regression functions and selected the best fitting for every grid point. Using a climate projection as a supplement in training the regression functions accounts for climate change and empowers the method’s application in future climate periods. The method includes a simple bias adjustment (delta change). Its output is regridded to ERA5 Land’s 0.1∘ grid, adapting it to the local environment and offering better application in areas with complex terrain using coarse data. Furthermore, the presented method and its regression coefficients can be created from any data set, allowing an even higher spatial resolution than ERA5 Land’s. While the method performs best for the temperature-related indices in warm temperate climates, it works uniformly well for the precipitation-related index maximum consecutive dry days on a global scale.

Trend Analysis for Temperature and Precipitation Extreme Events over the Headwater of Upper Blue Nile, Ethiopia

Purpose: Increased trends in extreme climate events such as prolonged hot or cold days and intensively rainy days or consecutive dry days have greater negative impacts on society. While studies are conducted on climate variability and impacts most of them focus on the mean climatologies. Extrem events of Tempertaure and preciptation however have profound effect on human health and the environment. The present study aims to examine the spatial and temporal characteristics of means and extremes of temperature and precipitation events in the Tana-Beles Sub-basin for the 1980-2015 periods. Methodology: Daily temperature and preciptaion time series was collected from the Ethiopian meteorological institute covering the 1980-2015. From the daily climate variables 22 daily extreme climate indices are calculated based on the Expert Team on Sector-specific Climate Indices (ET-SCI) definitions. Findings: The Monthly, seasonal and annual temperatures show mostly increasing trends. The mean annual maximum and minimum temperatures increased slightly, respectively, by 0.047 and 0.014 oC/yr. The frequency of cold days/nights has reduced by 33/38 days/nights annually; whereas, the frequency of warm days/nights has increased by 91/83 days/nights. The mean monthly, seasonal and annual precipitations also show mostly increasing trends.Among the selected precipitation indices the number of very heavy rain days (R20mm), Simple Daily Intensity Index (SDII), very wet days exceeding the 95th percentile (R95p), and annual total wet days precipitation (RCPTOT) shows increasing trend; whereas, only the maximum 1-day precipitation (Rx1day), show an insignificant decreasing trend. Precipitation during the study period shows high variability spatially and temporally. Unique Contribution to Theory, Policy and Practice: The study reveals increasing in number of hot days and vraible prciptation events, hence measures must be taken to reduce risks of extreme tempertures such as direct effect and indirect malaria distributions to new areas that have been cold before, and agricultural advised forming is also suggested to cope with the variabilities of prciptation. Keywords: Climate trends; extremes climate events; Temperature and rainfall; and Tana-Beles Sub basins.

Climatological trends of mean and extreme daily precipitation in Arizona (USA)

Precipitation in Arizona, USA, represents a fundamental resource for the needs of agriculture, people, and the environment. However, due to the risk associated with flash flooding, extreme precipitation also constitutes a natural hazard. Available research on precipitation in Arizona has been limited in scope and focus and there currently exists no comprehensive assessment of statewide, historical, mean, and extreme precipitation. We use daily precipitation records from 43 Global Historical Climate Network daily (GHCNd) weather stations to examine trends in mean and extreme precipitation across Arizona from 1950 to 2020. We use a suite of standardized precipitation indices and explore the statistical significance of historical changes at the annual, monthly, and seasonal scales. Our analysis returns a motley collection of results displaying a great degree of spatial variability and sensitivity to the temporal scale of analysis. Mean total precipitation at the statewide scale underwent a small decreasing trend of 0.15 mm year−1, with considerable interannual variability being a dominant feature. The majority of stations experienced no statistically significant change in extreme precipitation at the annual scale. Monsoon season mean precipitation followed a similar pattern to annual means, with an overall reduction of 0.14 mm year−1 across the state. One fourth of stations, all located at elevations lower than 650 m, recorded decreases in monsoon precipitation intensity. Winter season analysis presents a different picture, displaying a positive statewide trend of 0.11 mm year−1 in mean precipitation. One tenth of stations recorded increases in extreme precipitation intensity and decreases in the number of consecutive dry days. Results of our observational analysis indicate a lack of a clear consensus on the climatological trends of mean/extreme precipitation in Arizona during the study period. Our findings are in contrast to those from other similarly arid areas across the world and highlight the role of regional differences in modulating the potential hydrometeorological impacts associated with climate change. However, while we do not find widespread, significant, modification in the spectrum of precipitation changes examined, the hydrologic system of the state has been – and will continue to be - impacted by the ongoing temperature increase associated with the buildup of greenhouse gases and continued population growth.

Projection of Future Climate Change and Its Influence on Surface Runoff of the Upper Yangtze River Basin, China

Global climate change will modify precipitation and temperatures’ temporal and spatial distribution, trigger more extreme weather events, and impact hydrological processes. The Yangtze River basin is one of the world’s largest basins, and understanding future climate changes is vital for water resource management and supply. Research on predicting future climate change in the upper Yangtze River basin (UYRB) and introducing machine learning algorithms to analyze the impact of climate factors, including extreme weather indicators, on surface runoff is urgently needed. In this study, a statistical downscaling model (SDSM) was used to forecast the future climate in the UYRB, and the Mann–Kendall (MK) or modified Mann–Kendall (MMK) trend test at a 5% level of significance was applied to analyze temporal trends. The Spearman rank correlation (SRC) test at a 5% level of significance and random forest regression (RFR) model were employed to identify the key climatic factors affecting surface runoff from annual precipitation, annual temperature, maximum 5-day precipitation (R×5Day), number of tropical nights (TR), and consecutive dry days (CDD), and the RFR model was also used to predict future runoff. Based on the results, we found that, compared to the selected historical period (1985–2014), the mean annual precipitation (temperature) during the mid-term (2036–2065) increased by 18.93% (12.77%), 17.78% (14.68%), 20.03% (17.03%), and 19.67% (19.29%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively, and during the long term (2071–2100), increased by 19.44% (12.95%), 22.01% (21.37%), 30.31% (30.32%), and 34.48% (37.97%), respectively. The warming and humidification characteristics of the northwestern UYRB were more pronounced. The key climatic factors influencing surface runoff were annual precipitation, maximum 5-day precipitation (R×5day), and annual temperature. Because of warming and humidification, surface runoff in the UYRB is expected to increase relative to the historical period. The surface runoff during the mid-term (long term) increased by 12.09% (12.58%), 8.15% (6.84%), 8.86% (8.87%), and 5.77% (6.21%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. The implementation of sustainable development pathways under the low radiative forcing scenario can be effective in mitigating climate change, but at the same time, it may increase the risk of floods in the UYRB.

Temporal and Spatial Variations of Extreme Climate Events in Northwestern China from 1960 to 2020

In the context of global warming, the frequency and intensity of extreme weather and climate events have been increasing. Characterized by scarce water resources and fragile ecosystems, Northwest China has experienced a climate shift since the 1980s. In this study, spatial and temporal patterns of changes in the indices of climate extremes, based on daily maximum and minimum temperature and precipitation at 172 meteorological stations in Northwest China, were analyzed for the period 1960–2020. A total of 26 indices divided into two categories, 16 extreme temperature indices and 10 extreme precipitation indices, were used. Analysis of these indices revealed a general warming trend in the region, which consistent with global warming. The regional occurrence of summer days, tropical nights, growing season length, warm nights, warm days, and warm spell duration index increased by 0.22, 0.14, 0.29, 0.73, 0.46, and 0.11 days/decade, respectively. Over the same period, the occurrence of frost days, icing days, cool nights, cool days, and cold spell duration index decreased by −0.38, −0.21, −0.93, −0.44, and −0.13 days/decade, respectively. The decreasing trends in cold extremes were greater than the increasing trends in warm extremes. Additionally, many regions have experienced increasing trends in several precipitation indices. The annual total wet-day precipitation increased by 5.3 mm/decade. Increasing trends were also evident in simple daily intensity index, heavy precipitation days, very heavy precipitation days, very wet days, and extremely wet days. Consecutive dry days decreased by −1.5 days/decade, while no significant change was observed in consecutive wet days. In contrast to the remarkable spatial consistency of temperature extremes, precipitation extremes exhibited large and expected spatial variability. Most precipitation indices showed increasing trends in the western region of Northwest China and decreasing trends in the eastern part of Northwest China. These results indicate a transition from cold–dry to warm–wet in Northwestern China. Our findings suggest that Northwest China is experiencing more extreme climate events, which could consequently impact hydrological processes, ecological processes, and human health. These observations increase our understanding of the interactions between climate change and regional climate variability, which is conducive to improving disaster prevention.

Assessment of Hydrological Response to Climatic Variables over the Hindu Kush Mountains, South Asia

The impact of climate extremes, such as heat waves and extreme rainfall, can cause harvest failures, flooding, and droughts that ultimately threaten global food security, harming the region’s economy. Fluctuations in streamflow indicate the sensitivity of streamflow responding to extreme precipitation events and other climatic variables (temperature extremes) that play a significant role in its generation. Pakistan is also considered one of the climate change hotspot regions in the world. The devastating impacts have often occurred in recent decades due to an excess or shortage of streamflow, majorly generated from the Upper Indus Basin (UIB). To better understand climate extremes’ impact on streamflow, this study examined climate extremes and streamflow (Q) changes for three decades: 1990–1999, 2000–2009, and 2010–2019. Observed streamflow and meteorological data from nine sub-catchments across all climatic zones of the UIB were analyzed using RGui (R language coding program) and partial least squares regression (PLSR). Climatic variables were estimated, including precipitation extremes, temperature extremes, and potential evapotranspiration. The Mann–Kendal test was applied to the climatic indices, revealing that precipitation increased during the last 30 years, while maximum and minimum temperatures during the summer months decreased in the Karakoram region from 1990 to 2019. The spatiotemporal trend of consecutive dry days (CDD) indicated a more increasing tendency from 1990 to 2019, compared to the consecutive wet days (CWD), which showed a decreasing trend. PLSR was applied to assess the relation between climatic variables (extreme P, T indices, and evapotranspiration). It was found that the dominant climatic variables controlling annual streamflow include the r95p (very wet days) and R25mm (heavy precipitation days), maximum precipitation event amount, CWD, PRCPTOT (annual total precipitation), and RX5 (maximum five-day precipitation). The TXn (Min Tmax) and Tmax mean (average maximum temperature) dominate streamflow variables. Moreover, the impact of evapotranspiration (ET) on variations in streamflow is more pronounced in arid catchments. Precipitation is the predominant factor influencing streamflow generation in the UIB, followed by temperature. From streamflow quantification, it was found that climate-driven annual streamflow decreased during 1999–2019 in comparison to 1990–1999, with an increase in a few catchments like Kalam, which increased by about 3.94% from 2000 to 2010 and 10.30% from 2010 to 2019, and Shigar, which increased by 0.48% from 2000 to 2009 and 37.37% from 2010 to 2019 concerning 1990–1999. These variations were due to changes in these climatic parameters. The PLSR approach enables the identification of linkages between climatic variables and streamflow variability and the prediction of climate-driven floods. This study contributes to an enhanced identification and hydroclimatological trends and projections.