NASA Earth Exchange Global Daily Downscaled Projections Research Articles

High‐resolution climate change projection of northeast monsoon rainfall over peninsular India

AbstractIn this study, projected changes in mean northeast monsoon (NEM) rainfall and associated extreme rainfall and temperature events, over peninsular India (PI) and its six subdivisions, are quantified. High‐resolution dynamically downscaled simulations of the Weather Research and Forecasting (WRF) regional climate model driven by the boundary conditions from the Community Climate System Model version 4 (CCSM4) model (WRF‐CCSM4) are compared with statistically downscaled simulations of NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP). Over PI, these downscaled simulations show low bias in mean NEM rainfall (≤ − 0.44 mm·day−1) and high pattern correlation coefficient (≥0.75), giving confidence in their future projections. Under future warming over PI, both downscaled simulations project future significant enhancement in NEM rainfall with WRF‐CCSM4 projecting 1.98 mm·day−1 (83.78% change with respect to the present‐day mean) whereas the multimodel ensemble (MME) of eight NEX‐GDDP models project 0.67 ± 0.58 mm·day−1 (19.78%) by the midddle of the century and 1.42 ± 0.97 mm·day−1 (42.76%) by the end of the century. Analysis of extreme rainfall events shows that WRF‐CCSM4 projects future enhancement (reduction) in extreme rainfall (R95p) days over 91.4% (8.6%) of grid‐points over PI. In future, coastal areas of Karnataka and Andhra Pradesh will likely experience increased extreme rainfall occurrence by more than 25 days and 15–20 days respectively. Projected future enhancement in the mean and extreme NEM rainfall is attributed to the increased precipitable water under a warming climate. Future projection of extreme temperature indices shows an increase in minimum and maximum temperatures over PI during the NEM season. Over PI, future winter nights and days are found to be warmer than those in the present day and the temperature change in future winter nights is found to be larger than that in winter days. This climate change information would help decision‐makers in evaluating existing policies and devising revised policies to reduce risk due to climate change.

Climate Futures and Projected Mortality Due To Non-Optimal Temperature From 2020 To 2100: A Global Burden of Disease Forecasting Study

Background: A recent study estimated that 1·69 million (95% uncertainty interval [UI] 1·52–1·83) deaths were attributable to non-optimal temperature in 2019. Globally, most of this burden was attributed to low temperatures, but high heat-attributable mortality rates were observed in warmer regions such as south Asia, north Africa and the Middle East, and sub-Saharan Africa. The impact of increases in global temperatures and demographic trends on future temperature-related mortality is unclear. Methods: We linked cause-specific temperature-mortality risk curves to daily temperature forecasts from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset. The NEX-GDDP dataset comprises 21 individual General Circulation Models (GCM) that were spatially downscaled for two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5. For each pixel, day, and cause, we estimated the population attributable fraction (PAF) based on the risk of cause-specific mortality at a given temperature relative to the theoretical minimum risk exposure level (TMREL); we then estimated temperature-attributable deaths by multiplying these PAFs by location and cause-specific mortality estimates for the 1990 to 2020 reference period and by forecasted mortality estimates for the period from 2020 to 2100. Other upstream drivers in the mortality forecast model, including the Socio-demographic Index and air pollution levels, were held constant. We additionally estimated PAFs for a temperature scenario based on the 2015 Paris Agreement, assuming that countries meet their nationally determined contributions and continue to reduce emissions at the same rate after 2030. Findings: Globally, we estimate increases in the number and rate of deaths attributable to non-optimal temperature, with 2·26 million (1·66–2·94), 2·54 million (1·86–3·31) and 3·17 million (2·11–4·35) annual deaths by the end of the century, for the Paris scenario, and the RCP4.5 and RCP8.5, respectively. These increases are driven largely by heat-attributable burden, with smaller and more variable changes in cold-attributable deaths. Warm, low-latitude regions will be disproportionally affected, with the largest increases in temperature-attributable mortality expected in sub-Saharan Africa, south Asia, and Latin America and the Caribbean. Heat-attributable mortality was reduced by half for the Paris scenario relative to the RCP8.5 with 1·02 million (0·64–1·48) deaths versus 2·08 million (1·13–3·13) deaths in 2100. As our study accounts for adaptation and does not extrapolate relative risks beyond the exposures currently experienced, these estimates are likely conservative. Interpretation: Globally increasing temperatures are projected to substantially increase the mortality burden attributable to non-optimal temperature. Higher global temperatures only lead to minor decreases in cold-related mortality which do not offset increases in heat-related mortality. Funding: Bill & Melinda Gates Foundation Declaration of Interest: The authors declare that there are no conflicts of interest.

Application of CORDEX-AFRICA and NEX-GDDP datasets for hydrologic projections under climate change in Lake Ziway sub-basin, Ethiopia

Study regionLake Ziway sub-basin, Central Rift Valley basin, Ethiopia. Study focusThis study evaluated the applicability of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) and the Coordinated Regional Downscaling Experiment (CORDEX-AFRICA) datasets for climate change impact assessment. It also evaluated the hydrologic impact of climate change in the sub-basin. The hydrologic model Soil and Water Assessment Tool (SWAT) was forced with the climate datasets to simulate the reference and future period streamflows under RCP 4.5 and RCP 8.5 scenarios. The seasonality and volumetric deviations of the average annual streamflows simulated using the climate models were used as criteria for selecting members of the ensemble. New hydrological insights for the regionAll the climate models in the bias-corrected NEX-GDDP datasets failed to satisfy the criteria. The ensemble average annual streamflows, simulated using the CORDEX-AFRICA dataset, are expected to increase towards the end of the century under both the climate scenarios. However, the ensemble average dry season streamflows are projected to decrease during the mid- and end-periods of the century. The expected decreases in streamflows during the dry and pre-wet seasons, along with excessive abstractions of water from Lake Ziway, would exacerbate water scarcity in the basin. Approaches need to be developed to capture the increase in water availability during the wet season to alleviate water scarcity during the dry season.

Evaluation and projection of daily maximum and minimum temperatures over China using the high-resolution NEX-GDDP dataset

A new bias-corrected, statistically downscaled product, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset, has been developed and released to help in understanding climate change at local to regional scales. Here, we evaluate the performance of the NEX-GDDP data in simulating daily maximum temperature (TX) and daily minimum temperature (TN) in the historical period 1961–2005 over China at national and regional scales. Projected future changes in TX and TN are assessed under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 emissions scenarios. Results show that the NEX-GDDP data can capture the basic spatial patterns of TX and TN, but these results underestimate the warming trends of TX and TN from 1961 to 2005 over China. The largest biases are found in western China due to its complex terrain conditions; these biases are 2.33 and 2.21 times larger than those found in eastern China for TX and TN, respectively. The climate projections show that the difference in uncertainties is small between the east and the west, and higher warming changes correspond to greater uncertainties. The increasing trends under the RCP8.5 are 2.22 and 2.31 times the size found under the RCP4.5 by the end of the twenty-first century for TX and TN, respectively. The Tibetan plateau has the fastest warming trend under the two scenarios.

Double increase in precipitation extremes across China in a 1.5 °C/2.0 °C warmer climate

Paris Agreement's 1.5 °C or 2.0 °C global warming targets call for human concerns on warming climate on human society and environment in general. Here we analyzed spatiotemporal patterns and related impacts of precipitation extremes on human society across China using NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections) dataset. We found increasing trends of almost all extreme precipitation indices except consecutive dry duration (CDD). Additional 0.5 °C warmer climate from 1.5 °C to 2.0 °C global warming targets can double increase of extreme precipitation indices. Specifically, the increase of Rx5day (Max 5-day precipitation amount) is from 3.98% to 7.63%, the increase of R95pTOT (precipitation in very wet days) is from 19.41% to 34.42% and the increase of PRCPTOT (annual total wet-day precipitation) is from 3.89% to 8.23%, showing that additional 0.5 °C warmer climate can potentially increase flood risks across China. While, we also found regional differences in responses of extreme precipitation to warming climate. Extreme precipitation in the Qinghai Tibet Plateau, the Western Arid and semiarid zone and in the lower Yangtze River basin is in higher sensitivity to warming climate. Constraint of temperature increase of below 1.5 °C but not 2.0 °C will avoid 4.34% to 73.96% impacts of extreme precipitation on human society. It is particularly important for China since that more than half of territory of China is under exposure to high flood and drought disasters.

Future changes in precipitation extremes during northeast monsoon over south peninsular India

It is well recognized that the global climate has been warming and it can have adverse regional impacts in the future. Besides producing changes in mean climate, global warming can also substantially alters the weather and climate extremes. Considering the impact of weather extremes on agriculture as well as water resource management in an agrarian country like India, present study aims to characterize future changes in precipitation extremes in northeast monsoon season (October–November–December) over south peninsular India based on statistically downscaled high-resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) datasets. This NEX-GDDP dataset simulates the mean rainfall reasonably well. There may be an increase in precipitation in near (5% under RCP 4.5 and 11% under RCP 8.5) and far future (21% and 38% under RCP4.5 and RCP 8.5 respectively). Future climate projections indicate that both the intensity and frequency of precipitation extremes in most parts of the south peninsular India may increase under both the warming scenarios during northeast monsoon season.

Landscape evolution of the Wenchuan earthquake-stricken area in response to future climate change

The Wenchuan earthquake caused rapid and gradual changes to the local geomorphology in the earthquake-stricken area. Extreme rainfall events can have strong impacts on local geomorphic evolution and alter the risks of subsequent geohazards and flooding. However, there is still lack of understanding of long-term geomorphic and fluvial evolution in face of changing future climate. In this paper, we first perform the future extreme rainfall projection and then analyze the landscape evolution variation under future climate change with the CAESAR-Lisflood model. A new approach to applying the ‘NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP)’ dataset at the local scale is presented, and a future landscape evolution analysis is conducted. We used spatial (tailoring process) and statistical temporal downscaling methods with NEX-GDDP data to project future extreme rainfall in combination with the CAESAR-Lisflood model to simulate the landscape evolution in response to climate change. The results showed that the future rainfall projection model can provide the most robust and effective representative of future climate change scenarios at the local scale, the geological and geomorphological environment of the basin continues to change dynamically after the earthquake, and nearly 27 years (~2035) are needed before the geomorphology of the basin stabilizes, and then, the yearly yield remains at an average rate of 1 × 104 m3. Post-earthquake mountainous area response and recovery characteristics to a changing future climate can provide important information on future geohazard projections and measures to be taken.

Extreme hot days over three global mega‐regions: Historical fidelity and future projection

AbstractUsing a downscaled high‐resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) dataset based on Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, this study evaluated and compared extreme hot days (EHDs) over the three mega‐regions [the Eastern United States (EUS), Europe (EU) and Eastern Asia (EA)] during the historical period (1981–2005) against observations, resulting in a subset of models with high skill for the past climatology and trend. The observed EHDs over EU exhibit the largest absolute amount and the most significant increases in frequency (4.0 days·decade−1), intensity (0.22°C·decade−1) and extent (8.4°C·days·ecade−1), while no significant trend over EUS is found. Compared with the observation, the largest bias in NEX‐GDDP is the remarkably overestimated increase in the trend over EUS. In the RCP8.5 projection using six models with relatively high fidelity, the increase of EHDs is evidently enhanced during 2030–2054 over the three regions, particularly EUS. The projected trend of EHDs over EUS could be undetermined because of the modelling biases in aerosol effects and internal variation, which is worthy of further investigation in CMIP6.

Prediction of the karstic spring flow rates under climate change by climatic variables based on the artificial neural network: a case study of Iran.

Few studies have evaluated the impact of climate change on groundwater resources for a region with no pumping well. Indeed, the uncertainty of pumping wells may undesirably influence the results. Therefore, a region without any pumping well was selected to assess the impact of climate change on the karstic spring flow rates. NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset was used to extract the climatic variables for the present (1961-1990) and future (2021-2050) time periods by two Representative Concentration Pathways (RCPs), i.e., RCP4.5 and RCP8.5, in Lali region, southwest Iran. Although this dataset has been already verified, its output was evaluated for Lali region. Then, the impact of climate change on the discharge of Bibitarkhoun karstic spring was examined by the Artificial Neural Network (ANN). In this regard, if considering the daily data, ANN is not trained satisfactorily, because of the spring's lag time response to the precipitation; if monthly time step is considered, the data would not be adequate. Therefore, the average of some previous days was considered to calculate the variables. The average precipitation is 344, 329, and 324mm/year and the average temperature is 14.18, 15.98, and 16.3°C both for the present, future time period under RCP4.5 and future time period under RCP8.5, respectively. The network selected demonstrated no climate change impact on the average of spring discharge. However, the discharge increased by about + 8% in spring and summer and decreased by about - 7% in autumn and winter in the future time period.

Heatwave Trends and the Population Exposure Over China in the 21st Century as Well as Under 1.5 °C and 2.0 °C Global Warmer Future Scenarios

Heatwaves exert negative socio-economic impacts and particularly have serious effects on public health. Based on the multi-model ensemble (MME) results of 10 downscaled high-resolution Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) model output from NASA Earth Exchange Global Daily Downscaled Projections (NASA-GDDP), the intensity (largest lasting time), frequency and total duration of heatwaves over China as well as population exposure in the 21st century and at 1.5 °C and 2.0 °C above pre-industrial levels are investigated by using the three indices, the Heat Wave Duration Index (HWDI), annual total frequency of heatwaves (N_HW) and annual total days of heatwaves (T_HW) under RCP4.5 and RCP8.5. The MME results illustrate that heatwaves are projected to become more frequent (0.40/decade and 1.26/decade for N_HW), longer-lasting (3.78 days/decade and 14.59 days/decade for T_HW) as well as more extreme (1.07 days/decade and 2.90 days/decade for HWDI under RCP4.5 and RCP8.5 respectively) over China. High latitude and high altitude regions, e.g., the Tibetan Plateau and northern China, are projected to experience a larger increase of intensity, frequency and the total time of heatwaves compared with southern China (except Central China). The total population affected by heatwaves is projected to increase significantly and will reach 1.18 billion in later part of the 21st century, and there will be more and more people expected to suffer long heatwave time (T_HW) in the 21st century. Compared with a 2.0 °C global warming climate, holding the global warming below 1.5 °C can avoid 26.9% and 29.1% of the increase of HWDI, 34.7% and 39.64% for N_TW and 35.3%–40.10% of T_HW under RCP4.5 and RCP8.5 respectively. The half-degree less of warming will not only decrease the population exposure by 53–83 million but also avoid the threat caused by longer heatwave exposure under the two scenarios. Based on the comprehensive assessment of heatwave under the two RCP scenarios, this work would help to enhance the understanding of climate change and consequent risk in China and thus could provide useful information for making climate adaptation policies.

Advantage of NEX-GDDP over CMIP5 and CORDEX Data: Indian Summer Monsoon

Recently released NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data (surface air temperature and precipitation) are evaluated using the observations from the India Meteorological Department (IMD) and compared with CMIP5 and CORDEX data to show its advantage over the Indian subcontinent for the summer monsoon season. The multi-model mean of 21 NEX-GDDP models, 28 CMIP5 models, and 10 CORDEX models are analyzed for the period of 1975–2005. In general and over most parts of the subcontinent, the NEX-GDDP is found to be quite satisfactory and surpasses the CMIP5 and CORDEX data. The NEX-GDDP data captures the spatial patterns of seasonal mean temperatures and precipitation with highest accuracy (pattern correlation of ~0.8) and least errors (root mean square error of ~ 4.25 °C and ~2.48 mm day−1); the inter-annual variations in precipitation are closer to the observations (r = 0.66 and standard deviation = 0.36 mm day−1); bias in the annual cycle reduces; simulation of extremes is more realistic with less inter-model differences. Remarkable improvements in this data suggest its potential application for future projections and climate change impact studies at the regional scale.

Observed, simulated and projected extreme climate indices over Pakistan in changing climate

This article explores the observed, simulated and projected extreme climate indices from daily observed dataset for several meteorological stations as well as NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset. The NEX-GDDP dataset is an extension of the General Circulation Model (GCM) runs, performed under the Coupled Model Inter-comparison Project Phase 5 (CMIP5). Two greenhouse gas emission scenarios have been considered, known as Representative Concentration Pathways, i.e., RCP4.5 and RCP8.5. The core climate indices, proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI), were used for investigation of climatic extremes over different provinces of Pakistan. The results show that summer days have significantly increased in the Gilgit Baltistan and Azad Jammu Kashmir (GB-AJK) region by 24 and 18 days, respectively, triggering the frequency of Glacial Lake Outburst Flood (GLOF) events in the region. Moreover, the projected climate shows an increase of 6 days in the frequency of summer days in the GB-AJK region in the next 30 years. The frequency of heavy precipitation is significantly increasing in the Khyber Pakhtunkhwa (KP) province, which may augment the flood hazard in the region. Future projection shows an increase of 1.3 °C in the mean maximum temperature in the KP province by the year 2045. An average increase of 1.9 °C in the mean minimum temperature has been observed in the Punjab province over the past 54 years (1960-2013), whereas there is a projected increase of 1.4 °C in the mean minimum temperature in the next 30 years. Such an increasing trend in the mean minimum temperature may cause implications for food and agriculture sectors in the Punjab province. The results show a significantly increasing trend in the mean maximum temperature and summer days in the Sindh province, which may enhance the frequency and intensity of heat waves in the region. There is a projected increase of 20 days in summer days in the Sindh province. Similar rising trend in the temperature has been observed in the Balochistan province, which may ultimately enhance the dust and sand storms in this region.

Future Climate Change Projections of the Kabul River Basin Using a Multi-model Ensemble of High-Resolution Statistically Downscaled Data

In this study, we examined the future climatic changes in the Kabul River basin located in the Hindu Kush Mountain ranges of Pakistan and Afghanistan. We used the latest data set of statistically downscaled CMIP5 Global Climate Models (GCMs), i.e., NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). The data set delivers valuable local scale, high-resolution climate change information for past and future periods (1950–2100) on a daily basis, which is very suitable for exploring future changes in mean and extremes of both temperature and precipitation. Multi-model ensemble derived from NEX-GDDP data effectively produces observed spatial patterns and magnitude of both temperature and precipitation that otherwise cannot be captured with coarse resolution GCMs. For the historical period (1975–2005), NEX-GDDP presented an improved seasonal cycle climatology and correlation coefficient with the observed data set. Future projections using multi-model ensembles indicate a consistent rise in mean temperature over the entire Kabul River Basin, relative to the baseline under RCP4.5 and RCP8.5 emission scenarios. Although the increase in temperature is not uniform across the domain, upper reaches of the basin show annual and seasonal warming of approximately 6.8 °C by the end of the twenty first century under the RCP8.5 scenario. These changes are significant at a 95% confidence level. The rise in summer and winter temperatures may negatively affect the snow accumulation during winter and has the potential to accelerate glacier melting during summers. Projections of future precipitation under both scenarios show an overall decrease in mean precipitation, particularly under the RCP8.5 scenario.

Centennial Heat Wave Projections Over Pakistan Using Ensemble NEX GDDP Data Set

Heat waves are one of the extreme weather events affecting as many people as with other climatic hazards such as droughts and floods. Due to rise in global temperatures, the occurrences of heat waves are likely to exacerbate in many regions in future. The projections for future heat waves over Pakistan are analyzed in this study. The heat waves calculation has been done using the latest NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) data set which composed of 21 Global Climate Models (GCMs) statistically downscaled at 0.25° × 0.25° spatial resolution. The heat waves analysis is done with ensemble modelling approach and using two definitions of heat waves: the Heat waves Duration Index (HWDI) and fixed method approach. Two climate change scenarios; RCP4.5 and RCP8.5 are used for investigating future heat wave projections. The historical heat waves are calculated from 1976–2000 and future projected changes are analyzed in three 25 year time spans: 2025–2049, 2050–2074 and 2075–2099. Two metrics heat wave events and heat wave days are separately shown. The results indicate an increase in both number of heat wave events and heat wave days in Pakistan. The most vulnerable areas for future heat waves are northern areas, plains of Sindh and Punjab, the central and western parts of Baluchistan, and all regions of Khyber Pakhtunkhwa (KPK). The increased number of heat waves in northern areas of the country could result in rapid snow melting and can cause flooding downstream. The agricultural regions of the country would be highly vulnerable to increasing number of heat waves.

Historical simulations and climate change projections over India by NCAR CCSM4: CMIP5 vs. NEX-GDDP

A new bias-corrected statistically downscaled product, namely, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), has recently been developed by NASA to help the scientific community in climate change impact studies at local to regional scale. In this work, the product is validated over India and its added value as compared to its CMIP5 counterpart for the NCAR CCSM4 model is analyzed, followed by climate change projections under the RCP8.5 global warming scenario using the two datasets for the variables daily maximum 2-m air temperature (Tmax), daily minimum 2-m air temperature (Tmin), and rainfall. It is found that, overall, the CCSM4-NEX-GDDP significantly reduces many of the biases in CCSM4-CMIP5 for the historical simulations; however, some biases such as the significant overestimation in the frequency of occurrence in the lower tail of the Tmax and Tmin still remain. In regard to rainfall, an important value addition in CCSM4-NEX-GDDP is the alleviation of the significant underestimation of rainfall extremes found in CCSM4-CMIP5. The projected Tmax from CCSM4-NEX-GDDP are in general higher than that projected by CCSM4-CMIP5, suggesting that the risks of heat waves and very hot days could be higher than that projected by the latter. CCSM4-NEX-GDDP projects the frequency of occurrence of the upper extreme values of historical Tmax to increase by a factor of 100 towards the end of century (as opposed to a factor of 10 increase projected by CCSM4-CMIP5). In regard to rainfall, both CCSM4-CMIP5 and CCSM4-NEX-GDDP project an increase in annual rainfall over India under the RCP8.5 global warming scenario progressively from the near term through the far term. However, CCSM4-NEX-GDDP consistently projects a higher magnitude of increase and over a larger area as compared to that projected by CCSM4-CMIP5. Projected daily rainfall distributions from CCSM4-CMIP5 and CCSM4-NEX-GDDP suggest the occurrence of events that have no historical precedents. Worth noting is that the extreme daily rainfall values projected by CCSM4-NEX-GDDP are two to three times larger than that projected by CCSM4-CMIP5.

Characterization of future drought conditions in the Lower Mekong River Basin

This study evaluates future changes to drought characteristics in the Lower Mekong River Basin using climate model projections. The Lower Mekong Basin (LMB), covering Thailand, Cambodia, Laos and Vietnam, is vulnerable to increasing droughts. Univariate analysis was employed in this study to compare drought characteristics associated with different return periods for the historical period 1964–2005 and future scenarios (RCP 4.5 2016–2057, RCP 4.5 2058–2099, RCP 8.5 2016–2057 and RCP 8.5 2058–2099). Because a single drought event is defined by several correlated characteristics, drought risk assessment by a multivariate analysis was deemed appropriate, and a multivariate analysis of droughts was conducted using copula functions to investigate the differences in the trivariate joint occurrence probabilities of the historical period and future scenarios. The Standardized Precipitation Index (SPI) was selected as the drought index because of its ability to detect and compare metrological droughts across time and space scales. Historical precipitation data from 1964 to 2005 and future precipitation projections from 2016 to 2099 for 15 global circulation models (GCMs) obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset were employed. In all future scenarios, the Lower LMB and 3S subbasins were expected to experience more severe and intense droughts. The multivariate drought risk assessment revealed an increase in drought risks in the LMB. However, the Chi-Mun subbasin may experience an alleviation of future drought characteristics. Because the basin was expected to experience an increase in average monthly precipitation in most months, the variability in magnitude suggested that the LMB region requires adaptation strategies to address future drought occurrences.

Projection of China’s near- and long-term climate in a new high-resolution daily downscaled dataset NEX-GDDP

The projection of China’s near- and long-term future climate is revisited with a new-generation statistically downscaled dataset, NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections). This dataset presents a high-resolution seamless climate projection from 1950 to 2100 by combining observations and GCM results, and remarkably improves CMIP5 hindcasts and projections from large scale to regional-to-local scales with an unchanged long-term trend. Three aspects are significantly improved: (1) the climatology in the past as compared against the observations; (2) more reliable near- and long-term projections, with a modified range of absolute value and reduced inter-model spread as compared to CMIP5 GCMs; and (3) much added value at regional-to-local scales compared to GCM outputs. NEX-GDDP has great potential to become a widely-used high-resolution dataset and a benchmark of modern climate change for diverse earth science communities.