Accurate estimation of precipitation at the global scale is of utmost importance. Even though satellite and reanalysis products are capable of providing high spatial-temporal resolution estimations at the global level, their uncertainties vary with regional characteristics, scales, and so on. The uncertainties among the estimates, in general, are much higher at the sub-daily scale compared to daily, monthly and annual scales. Therefore, quantifying these sub-daily estimations is of specific importance. In this context, this study seeks to explore the diurnal cycle of precipitation using all the currently available space-borne and reanalysis-based precipitation products with at least hourly resolution (IMERG, GSMaP, CMORPH, PERSIANN, ERA5) at the quasi-global scale (60◦N - 60◦S). The diurnal variability of precipitation is estimated using three parameters, namely, the precipitation amount, frequency, and intensity, all remapped at a common resolution of 0.25◦ and 1 h. All the estimates well represent the spatio-temporal variation across the globe. Nevertheless, considerable uncertainties exist in the estimates regarding the peak precipitation hour, as well as the diurnal mean precipitation amount, frequency, and intensity. In terms of diurnal mean precipitation, PERSIANN shows the lowest estimates compared to the other datasets, with the largest difference observed over the ocean rather than over land. As for diurnal frequency, ERA5 exhibits the highest disparity among the estimates, with a frequency twice as high as that of the other estimates. Furthermore, as expected being based on model reanalysis, ERA5 shows an early diurnal peak and the highest variability compared to the other datasets. Moreover, among the satellite estimates, IMERG, GSMaP, and CMORPH exhibit a similar pattern with a late afternoon peak over land and an early morning peak over the ocean.

The study of the water cycle at planetary scale is crucial for our understanding of large-scale climatic processes. There have been numerous studies that quantified the water cycle and its components, i.e., precipitation, evaporation, and runoff, over the land and the ocean. However, very little is known about how water fluxes are distributed across regions with different climatic or land properties. Here, we address this gap by providing robust estimates for terrestrial precipitation over a suite of land cover types, biomes, elevation zones, and precipitation intensity classes. We achieve this by estimating the mean annual precipitation of a 17-dataset ensemble between 2000 and 2019 at 0.25° spatial resolution. Our estimate of annual terrestrial precipitation is at approximately 114 000 ± 9 400 km3, with about 70% falling over one third of the grid cells, 80% over the 0 – 800 elevation zone, and two-thirds over forested regions. Our results also highlight that despite the current progress in the development of global scale data products there are still substantial uncertainties over the arid and/or high-elevation areas. Bigger discrepancies appear within the reanalysis data products, while remote sensing estimates show a better agreement with the in-situ ground truth. These results help to detect regions of high observational fidelity and pave the way to further explore and improve observational uncertainties. At the same time, we provide consistent estimates that can be used for benchmarking the precipitation partition in the climate models, and most importantly that can be used to assess future changes in global precipitation.

The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein, we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then, we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor as consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia occur during spring, while the spatial pattern of the change in median values stems from summer changes in the water cycle, which are the seasons within the months with statistically significant changes.

AbstractThe study of the water cycle at planetary scale is crucial for our understanding of large-scale climatic processes. However, very little is known about how terrestrial precipitation is distributed across different environments. In this study, we address this gap by employing a 17-dataset ensemble to provide, for the first time, precipitation estimates over a suite of land cover types, biomes, elevation zones, and precipitation intensity classes. We estimate annual terrestrial precipitation at approximately 114,000 ± 9400 km3, with about 70% falling over tropical, subtropical and temperate regions. Our results highlight substantial inconsistencies, mainly, over the arid and the mountainous areas. To quantify the overall discrepancies, we utilize the concept of dataset agreement and then explore the pairwise relationships among the datasets in terms of “genealogy”, concurrency, and distance. The resulting uncertainty-based partitioning demonstrates how precipitation is distributed over a wide range of environments and improves our understanding on how their conditions influence observational fidelity.

The majority of global precipitation falls in tropical oceans. Nonetheless, due to the lack of in situ precipitation measurements, the number of studies over the tropical oceans remains limited. Similarly, the performance of IMERG products over the tropical oceans is unknown. In this context, this study quantitatively evaluates the 20 years (2001-20) of IMERG V06 Early, Late, and Final products against the in situ buoys' estimates using the pixel-point approach at a daily scale across the tropical oceans. Results show that IMERG represents well the mean spatial pattern and spatial variation of precipitation, though significant differences exist in the magnitude of precipitation amount. Overall, IMERG notably overestimates precipitation across the tropical ocean, with maxima over the western Pacific and Indian Oceans, while it performs better over the eastern Pacific and Atlantic Oceans. Moreover, irrespective of the region, IMERG sufficiently detects precipitation events (i.e., .0.1 mm day(-1)) for high-precipitation regions, though it significantly overestimates the magnitude. Despite IMERG's detection issues of precipitation events over the regions with lower precipitation, it is in good agreement with the buoys in total precipitation estimation. The positive hit bias and false alarm bias are the major contributions to the overall total positive bias. Furthermore, the detection capability of IMERG tends to decline with in-creasing precipitation rates. In terms of IMERG runs, the IMERG Final product performs slightly better than the Early and Late runs. More detailed studies over the tropical oceans are required to better characterize the biases and their sources.

Compound extreme events, such as flash drought, have received wide attention in recent decades due to their far-reaching effects on the ecosystem. Thus, a new concept of flash drought has begun to spread globally in the scientific community, and it is continuously being developed. This study offers for the first time an overview of the global trends in flash drought research from 2000 to 2021. The analysis was based on the Scopus database in order to investigate the publication trends, contributions, collaborations, and challenges on a global scale. Furthermore, collaboration analysis was performed to detect collaboration networks within the flash drought research field. A total of 76 studies were published during the study period. The research output grew exponentially with an average growth rate of 30% per year. The challenging issues in the field of flash drought research are the search for appropriate definition of flash drought, the development of effective early warning systems and the scarcity of high-resolution data. By presenting the details of the evolution of this new conceptualization in drought research, our study highlights the main pathways of scientific progress and stimulates future research.

Abstract. The water cycle in Czechia has been observed to be changing in recent years, with precipitation and evapotranspiration rates exhibiting a trend of acceleration. However, the spatial patterns of such changes remain poorly understood due to the heterogeneous network of ground observations. This study relied on multiple state-of-the-art reanalyses and hydrological modeling. Herein we propose a novel method for benchmarking hydroclimatic data fusion based on water cycle budget closure. We ranked water cycle budget closure of 96 different combinations for precipitation, evapotranspiration, and runoff using CRU TS v4.06, E-OBS, ERA5-Land, mHM, NCEP/NCAR R1, PREC/L, and TerraClimate. Then we used the best-ranked data to describe changes in the water cycle in Czechia over the last 60 years. We determined that Czechia is undergoing water cycle acceleration, evinced by increased atmospheric water fluxes. However, the increase in annual total precipitation is not as pronounced nor consistent as evapotranspiration, resulting in an overall decrease in the runoff. Furthermore, non-parametric bootstrapping revealed that only evapotranspiration changes are statistically significant at the annual scale. At higher frequencies, we identified significant spatial heterogeneity when assessing the water cycle budget at a seasonal scale. Interestingly, the most significant temporal changes in Czechia take place during spring, while median spatial patterns stem from summer changes in the water cycle.

The tropical ocean, which receives a substantial volume of precipitation and evapotranspiration, has a greater impact on Earth's energy and water balance. Yet, considerable studies have been focused on the Earth's terrestrial precipitation, whereas very little attention has been given to the oceanic region. Despite the advancement of satellite and reanalysis precipitation estimates, relatively few studies explored these estimates over the oceans. In this context, we quantitatively evaluate and inter-compare the state-of-the-art satellite, reanalysis, and merged precipitation products over the tropical oceans. We use a suite of various gridded and well-known precipitation datasets such as the Integrated Multisatellite Retrieval for Global Precipitation Measurement (IMERG), Global satellite Mapping of Precipitation (GSMaP), European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis v5 (ERA5), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) products to comprehensively estimate the tropical oceanic precipitation and its spatial and temporal variation. In particular, we are looking into the estimates of the total mean precipitation and its inter-annual variation. In addition, the discrepancies among various precipitation estimates are analysed as a function of different spatial and temporal scale to assess their uncertainty over tropical ocean for the first time. This study will provide deep insights into the precipitation characteristic and its spatio-temporal variability across the tropical ocean. Moreover, such information will help to revisit the estimation of the global water budget components in the near future.

Accurate, reliable, and high spatio-temporal resolution precipitation data are vital for many applications, including the study of extreme events, hydrological modeling, water resource management, and hydroclimatic research in general. In this study, we performed a systematic review of the available literature to assess the performance of the Integrated Multi-Satellite Retrievals for GPM (IMERG) products across different geographical locations and climatic conditions around the globe. Asia, and in particular China, are the subject of the largest number of IMERG evaluation studies on the continental and country level. When compared to ground observational records, IMERG is found to vary with seasons, as well as precipitation type, structure, and intensity. It is shown to appropriately estimate and detect regional precipitation patterns, and their spatial mean, while its performance can be improved over mountainous regions characterized by orographic precipitation, complex terrains, and for winter precipitation. Furthermore, despite IMERG's better performance compared to other satellite products in reproducing spatio-temporal patterns and variability of extreme precipitation, some limitations were found regarding the precipitation intensity. At the temporal scales, IMERG performs better at monthly and annual time steps than the daily and sub-daily ones. Finally, in terms of hydrological application, the use of IMERG has resulted in significant discrepancies in streamflow simulation. However, and most importantly, we find that each new version that replaces the previous one, shows substantial improvement in almost every spatiotemporal scale and climatic condition. Thus, despite its limitations, IMERG evolution reveals a promising path for current and future applications.

A major proportion of the global precipitation falls at the tropical oceans. Nonetheless, due to the lack of in-situ precipitation measurements, studies over the ocean and so over the tropical oceans remain limited. Among others, the Integrated Multi-Satellite Retrievals for GPM (IMERG) is currently one of the best satellite estimates and has been widely applied in various research applications. However, its performance over the ocean, and specifically, over the tropical oceans is yet to be known. Thus, in this study, we quantitatively evaluate the IMERG V06 Early, Late and Final products using along-track shipboard data (OceanRain dataset) and in-situ data (buoy observations from the Global Tropical Moored Buoy Array; GTMBA) across the tropical oceans. The GTMBA data involve the Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) in the Pacific, the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA), and the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA) in the Indian Ocean. We examine the IMERG error characterization and bias distribution across the daily, monthly, and seasonal scales over the tropical oceans. Subsequently, we investigate the IMERG performance for light and extreme precipitation, both in terms of intensity and frequency. The evaluation of the IMERG data with OceanRain and buoys constitute both point-area and grid-grid based approaches. The categorical indices, which used to evaluate the detection capability of IMERG include the Probability of Detection (POD), the False Alarm Ratio (FAS) and the Critical Success Index (CSI). This study will bring out important information for the user community, the GPM ground validation group, and algorithm developers regarding the IMERG performances and thus its applicability over an ‘untraditional’ region such as oceans. Key words: GPM, IMERG, Precipitation, OceanRain, Buoys, Remote sensing

There is general agreement about the water cycle acceleration in the community, although its strength over land has been debated lately. While some common behavior is observed under similar climatic conditions across the globe, at the regional scale the water cycle's response to global warming is specific to its location's unique characteristics. Herein, we quantify the water cycle and characterize its climatology over the Czech Republic, which constitutes an essential headwaters area of the European continent, and in hydrological terms, it can be called the “roof of Europe”. The country's location involves three drainage catchments: the Elbe, Oder, and Danube rivers, which lead to the North Sea, the Baltic Sea, and the Black Sea respectively. Our analysis includes various data sets at different spatiotemporal scales like: The Twentieth Century Reanalysis (20CR), CPC Merged Analysis of Precipitation (CMAP), CPC Global Unified Gauge-Based Analysis of Daily Precipitation (CPC), Climatic Research Unit gridded Time Series (CRU TS), Global Historical Climatology Network monthly (GHCN), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), Global Precipitation Climatology Centre (GPCC), The Global Precipitation Measurement Integrated Multi-satellite Retrievals (GPM IMERG), Global Runoff Data Centre (GRDC), Global Runoff Reconstruction (GRUN), Moderate Resolution Imaging Spectroradiometer Terra Net Evapotranspiration (MOD16A2), National Centers for Environmental Prediction DOE Reanalysis 2 (NCEP DOE), National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP NCAR), NOAA's Precipitation Reconstruction over Land (PRECL), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TRMM 3B43), and University of Delaware Precipitation (UDEL). To exploit the availability of the various data sets for each component of the water cycle we merged them via simple weighted averages, a multi-source data integration method that has proven to be effective and with low computational requirements. Subsequently, we linked the computed components constraining them by the water budget equation. Thereafter, the time series were analyzed to quantify trends and their statistical significance, as well as their uncertainty derived by the multiple datasets. In addition to the time series analysis and the statistics involved so far, a spatial analysis explored the water cycle climatology and its variability over the whole Czech Republic and then its behavior in subdomains defined by the watersheds within the borders of the country.

Analysis of spatial and temporal changes of long-term precipitation and extreme precipitation distribution at a local scale is very important for the prevention and mitigation of water-related disasters. In the present study, we have analyzed the long-term trend of 116 years (1901–2016) of precipitation and distribution of extreme precipitation index over the Kosi River Basin (KRB), which is one of the frequent flooding rivers of India, using the 0.25° × 0.25° resolution gridded precipitation datasets obtained from the Indian Meteorological Department (IMD), India. The non-parametric Mann–Kendall trend test together with Sen’s slope estimator was employed to determine the trend and the magnitude of the trend of the precipitation time series. The annual and monsoon seasons revealed decreasing trends with Sen’s slope values of −1.88 and −0.408, respectively. For the extreme indices viz. R10 and R20 days, a decreasing trend from the northeastern to the southwest part of the basin can be observed, whereas, in the case of highest one-day precipitation (RX1 day), no clear trend was found. The information provided through this study can be useful for policymakers and may play an important role in flood management, runoff, and understanding related to the hydrological process of the basin. This will contribute to a better understanding of the potential risk of changing rainfall patterns, especially the extreme rainfall events due to climatic variations.

Soil loss through erosion and its subsequent deposition is considered as an important challenge for watersheds. In this paper, attempt has been made to integrate the Revised Universal Soil Loss Equation, rainfall climatology from merged IMD gauge-TRMM (1998–2015) and soil hydraulic parameters to delineate the highly susceptible zones of the Kosi River Basin (KRB), Bihar, India for soil erosion assessment and watershed prioritization. The soil hydraulic parameters are calculated by using the ROSETTA model. Afterwards, the analytical hierarchy process based on multi-criteria evaluation method (AHP-MCE) was employed to assign the weighting to each factor (Soil erosion, Compound Factor, Field Capacity) depending on their erosion potential. Weighted overlay analysis is then performed to generate the watershed prioritization map for soil and water conservation. The overall findings suggest that the sub-watersheds 5, 8 and 7 required utmost attention and conservative measures because of their high erodibility characteristics.

The knowledge of global precipitation is of crucial importance to the study of climate dynamics and the global water cycle in general. Although global precipitation climatologies have existed for some time, and their understanding has improved dramatically due to the vast amount of different data sources, their information has not been comprehensive enough due to precipitation spatial-temporal variability. Thus, ground station reports are, in some cases, not representative of the surrounding areas. Remote sensing data and model simulations complemented the traditional surface measurements and offered unprecedented coverage on a global scale. It is important to note that satellite data records are now of sufficient time frame lengths and with methods “mature” enough to develop meaningful precipitation climatologies that are able to provide information on precipitation patterns and intensities on a global scale. While data (and in some cases exploration/visualization tools as well) are widely available, each dataset comes with different spatial resolution, temporal resolution, and biases. Consequently, this unique opportunity to obtain a robust quantification of global precipitation has been hindered by the uncertainty, already revealed in the first attempts of the unification of different data products. Herein, we present a multi-source quantification of global precipitation, focusing on the description of the underlying uncertainties. Our approach combines station (CRU, GHCN-M, PRECL, UDEL, and CPC Global), remote sensing (PERSIANN, PERSIANN-CCS, PERSIANN-CDR, GPCP, GPCP_PEN_v2.2, CMAP, and CPC-Global) and reanalysis (NCEP1, NCEP2, and 20CRv2) data products, providing an updated overview of the role of precipitation in global water cycle.

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon's and Penman-Monteith's methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.

Annual runoff is important information on water balance in the catchment and large river basin scale. It forms the boundary conditions for mathematical modelling of hydrological balance on a finer temporal and spatial scale. It is important for the assessment of climate change on water resources. Currently, there are several datasets on global gridded runoff fields available. GRUN and E-RUN provide monthly estimates of runoff rate with the spatial resolution of 0.5 degree. The GRUN is global dataset and E-RUN is covering Europe 1,2.In this study, we evaluate the capability of paleoclimate reconstructions on precipitation, PDSI, and temperature, which are available in the form of gridded fields, to estimate annual surface runoff using selected machine learning techniques. For this purpose, we use as a benchmark runoff information GRUN and E-RUN data sets. Both data are aggregated on the annual time scale for the period 1902 – 2014 (GRUN) and 1952-2015 (E-RUN). Following machine learning algorithms were tested: Random forests, SVM, MLP, LDA and Extra Trees. Reconstructed precipitation, temperature, PDSI3 and runoff estimated using selected Budyko models with different spatial aggregation served as inputs4–7 . Different combinations of inputs were analysed.Our results show that the estimated surface runoff is in good agreement with E-RUN and GRUN datasets for analysed periods. The result and newly tested approach based on derived machine learning models can be further applied to the estimation of paleoclimatic reconstructions of runoff fields. References: 1. Ghiggi, G., Humphrey, V., Seneviratne, S. I. & Gudmundsson, L. GRUN: an observation-based global gridded runoff dataset from 1902 to 2014. Earth Syst. Sci. Data 11, 1655–1674 (2019). 2. Gudmundsson, L. & Seneviratne, S. I. Observation-based gridded runoff estimates for Europe (E-RUN version 1.1). Earth Syst. Sci. Data 8, 279–295 (2016). 3. Cook, E. R. et al. Old World megadroughts and pluvials during the Common Era, Sci. Adv., 1, e1500561. (2015). 4. Schreiber, P. Über die Beziehungen zwischen dem Niederschlag und der Wasserführung der Flüsse in Mitteleuropa. Z Meteorol 21, 441–452 (1904). 5. Ol’Dekop, E. M. On evaporation from the surface of river basins. Trans. Meteorol. Obs. 4, 200 (1911). 6. Turc, L. Le bilan d’eau des sols: relations entre les précipitations, l’évaporation et l’écoulement. (1953). 7. Pike, J. G. The estimation of annual run-off from meteorological data in a tropical climate. J. Hydrol. 2, 116–123 (1964).

Accurate and reliable estimation of precipitation is essential for the understanding of the global hydrological cycle and Earth energy balance. Considering the sparse gauge distribution and other limitations, it is not always feasible to access precipitation data at every point of Earth. Although monitoring precipitation from space is an alternative to this, its performance across the globe is not comprehensive enough for hydrological applications. However, the recently launched GPM (Global Precipitation Measurement) is a multi-satellite network, providing globally more accurate and instantaneous observations of rain and snow at a very fine spatio-temporal resolution. Here, to gain a quantitative insight into the state of the art of GPM products, we explored approximately 100 peerreviewed scientific articles that evaluated the GPM products over different geographic and climatic conditions throughout the world. Our review aims to identify the advantages and disadvantages of the GPM products at global scale and assist the developers to make improvements in the future versions.

Remote sensing and Geographical Information System are efficient techniques for prioritization of sub-watershed through morphometric analysis and positioning water harvesting structures. The morphometric analysis provides a quantitative description of a watershed for evaluating and estimation of drainage characteristics. The present study focused on Kosi River Basin (KRB), situated in Indo-Gangetic plains, Bihar state, India for soil and water conservation. The Digital Elevation Model (DEM) estimated from Shuttle Radar Topographic Mission (SRTM) is utilized for calculating the morphometric parameters in terms of basic, linear, and shape parameters and compound factor. KRB have been divided into 13 sub-watersheds and prioritized accordingly based on compound factor analysis to meet the need of water and soil conservation. The overall results indicate that the sub-watershed nos. 5, 8 and 4 require utmost attention and conservative practices because of their high erodibility characteristics. This study can be very useful in the identification of erosion-prone areas and implementation of conservation practices for water resources.

The study analysed the changes in the rainfall, extreme indices and their future projections over Rajasthan state based on observed gridded datasets (1976–2005) and simulated climate models. The climate projections from two global circulation models (HadCM3 and GFCM21) are used in statistical downscaling tool LARS-WG5 (Long Ashton Research Station-Weather Generator) to generate future precipitation. Further, the changes in precipitation pattern are investigated for the baseline period and the future periods based on seven extreme precipitation indices. Three future periods are used for the analysis i.e., early century period 2011–2040 (2025s), a mid-century period of 2041–2070 (2055s) and a late-century period of 2071–2100 (2085s). The study area is classified in three regions based on elevation range i.e., region 1 (< 250 m), region 2 (251–350 m) and region 3 (350–1700 m). Based on results, it is observed that there is a possible decrease in monsoon precipitation at many grid points for all the three future periods. The maximum decrease in rainfall (−142 mm) is observed in Banswara for the period 2041–2070, while the maximum increase (37 mm) is found in Alwar along with Churu 1 and Ganganagar during the period 2071–2100. Consecutive dry days (CDD) is predicted to increase in the west and south-west direction, while it shows decrease values in eastern and central part of the study area with the maximum value in Ajmer district. The pattern in PRCPTOT revealed maximum negative change (− 90 mm) in southern parts, and maximum positive change in the northern regions (62.2 mm) in Churu 1. Further, R20 and RX5day are projected to decrease in all three regions in future with several magnitudes. For RX1day, a maximum positive change is observed in eastern parts (Jhalawar, Sawai Madhopur) and negative changes in the southern part of the study area. In case of R95p index, both positive and negative changes are observed. Similarly, the SDII indicates a positive change in 2011–2040 and negative changes for the remaining two future periods. Finally, SDII shows maximum positive changes in the south and southeastern regions (Jhalawar, Chittaurgarh) and positive changes in various parts with spatial and temporal changes. The results will help water resources planner to understand the change pattern in various precipitation indices in water scarce state of India.

The ongoing increases in concentrations of atmospheric greenhouse gas will most likely affect global climate for the rest of this century. Global warming brings a huge provocation to society and human beings. Single extreme events and increased climate variability have a greater impact than long-term changes in the mean of climatic variables. This study analyzed the temperature projections for Rajasthan state, India using data obtain from two General Circulation Models (GFCM21 and HadCM3) for three Intergovernmental Panel on Climate Change (IPCC) Special Range of Emission Scenarios (SRES) A1B, A2, and B1. A 30years of maximum (Tmax) and minimum (Tmin) temperature for the period 1976–2005 has been obtained from India Meteorological Department (IMD) and by using LARS-WG5 to generate the long-term weather series for three different periods i.e. 2011–2040 (2025s), 2041–2070 (2055s), and 2071–2100 (2085s). Further to determine the changes in extreme temperature events, the data for the baseline period and the future periods was represented by eight extreme temperature indices. Results illustrate that an increase in minimum and the maximum temperature are observed in all the three future periods. The average mean temperature for base period and three future periods over four regions of Rajasthan was observed highest in region 3 which shows an incessantly increased in mean temperature about 2.6°C i.e. north-east and north-west part of Rajasthan. Two GCMs depicts that the incessant temperatures may be increase in the future and future maximum temperature in all the seasons varies from 2.43°C to 4.27°C in the direction from south to north of Rajasthan during 2071–2100. While for minimum temperature, the range of temperature changes varies from 0.23°C to 1.42°C from south-east to north-west of Rajasthan during 2011–2040. In the temperature indices, the number of tropical nights (TR20), warmest day (TX90p), warmest night (TN90p) and summer days (SU25) is expected to increase during all three future periods. The maximum changes was found in region 2 (39.4days) and region 1 (38.8days) during the 2071–2100 periods, followed by 2041–2070 and 2011–2040. In all the four regions, the annual occurrence of Cold Spells Duration Indicator (CSDI) decreased and Warm Spells Duration Indicator (WSDI) increased for all three future periods.