In this study, simulations over Southeast Asia (15°S–40°N, 80°–145°E) at 36 km resolution were conducted for the period 1989–2007 using the Regional Climate Model version 4.3 (RegCM4.3) under the framework of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment – Southeast Asia (or SEACLID/CORDEX‐SEA) project. Forced by the European Centre for Medium‐Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA‐Interim), 18 experiments were carried out using different combinations of cumulus parameterization and ocean flux schemes. Twelve extreme indices for both rainfall and temperature were estimated from the model output. A statistical omega index was used to measure the degree of similarity among the 18 experiments in phase and shape. The results showed relatively high similarities among the experiments over mainland Asia compared to those over the Maritime Continent for both seasonal and inter‐annual variability. The extreme rainfall indices had a lower omega compared to that of temperature. Observed daily rainfall and temperature data at 52 meteorological stations over the SEA region were used to validate the simulated extreme indices. The results showed that extreme temperature indices were generally underestimated across the region. Systematic biases for each simulated rainfall index were also identified. A score ranking system was established to compare the relative performance of the 18 experiments over the 52 selected stations objectively. It was shown that the experiments with the Massachusetts Institute of Technology (MIT)‐Emanuel scheme performed relatively better than the other convective schemes. The combination of the MIT‐Emanuel convective scheme with the Biosphere–Atmosphere Transfer scheme (BATS1e) ocean flux scheme produced the best performance.

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In this study, daily-observed data from 481 rain gauges were used to build a new gridded rainfall dataset for Vietnam based on the Spheremap interpolation technique. The new dataset, called Vietnam Gridded Precipitation (VnGP) Dataset has the resolution of 0.25° and covers the period 1980−2010. The validation was done for VnGP by assessing the spatial distribution, correlations, mean abosolute errors, root mean square errors with gauge observations. Results showed that VnGP had a relatively better performance compared to the datasets that used different interpolation techniques or used less number of input rain gauges. VnGP is currently available at the Data Integration and Analysis System (DIAS) managed by the

With the acceleration in global warming, extreme hot temperatures have emerged as one of the most prominent risks. In this study, we characterize the unprecedented extreme temperatures that occurred in Korea in summer 2018, and attempt to explain how this locally observed extreme event can be interpreted in the context of 2°C and 3°C global warming above the pre-industrial level. To better resolve geographically diverse climate features and enhance confidence in future changes, three global projections are dynamically downscaled using three regional climate models that are customized over Korea and the systematic biases are statistically corrected using quantile mapping. In July and August 2018, abnormally high maximum temperatures (Tmax) were observed over the entire territory of South Korea. Beyond the increase of mean value, Tmax at individual stations departed significantly from the typical Gaussian distribution of climatological Tmax due to the dramatic changes in the extent and shape of upper tails. The distinct behaviors of Tmax that appeared in 2018 largely represent the statistical analog of the distribution pattern expected under 3°C global warming based on finescale climate projections. This implies that statistically extremely rare events like that of summer 2018 will become increasingly normal if global average temperature is allowed to increase by 3°C. More importantly, the extreme heat stress measured by the wet-bulb globe temperature is projected to intensify the risks to a level never before seen in contemporary climate. This study is timely and relevant to the need to identify how the globally aggregated warming target temperature can be disaggregated into regional impacts.

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This study investigates the ability to apply National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) products and their downscaling by using the Regional Climate Model version 4.2 (RegCM4.2) on seasonal rainfall forecasts over Vietnam. First, the CFS hindcasts (CFS_Rfc) from 1982 to 2009 are used to assess the ability of the CFS to predict the overall circulation and precipitation patterns at forecast lead times of up to 6 months. Second, the operational CFS forecasts (CFS_Ope) and its RegCM4.2 downscaling (RegCM_CFS) for the period 2012–14 are used to derive seasonal rainfall forecasts over Vietnam. The CFS_Rfc and CFS_Ope are validated against the ECMWF interim reanalysis, the Global Precipitation Climatology Centre (GPCC) analyzed rainfall, and observations from 150 meteorological stations across Vietnam. The results show that the CFS_Rfc can capture the seasonal variability of the Asian monsoon circulation and rainfall distribution. The higher-resolution RegCM_CFS product is advantageous over the raw CFS in specific climatic subregions during the transitional, dry, and rainy seasons, particularly in the northern part of Vietnam in January and in the country’s central highlands during July.

To investigate the ability of dynamical seasonal climate predictions for Vietnam, the RegCM4.2 is employed to perform seasonal prediction of 2 m mean (T2m), maximum (Tx), and minimum (Tn) air temperature for the period from January 2012 to November 2013 by downscaling the NCEP Climate Forecast System (CFS) data. For model bias correction, the model and observed climatology is constructed using the CFS reanalysis and observed temperatures over Vietnam for the period 1980–2010, respectively. The RegCM4.2 forecast is run four times per month from the current month up to the next six months. A model ensemble prediction initialized from the current month is computed from the mean of the four runs within the month. The results showed that, without any bias correction (CTL), the RegCM4.2 forecast has very little or no skill in both tercile and value predictions. With bias correction (BAS), model predictions show improved skill. The experiment in which the results from the BAS experiment are further successively adjusted (SUC) with model bias at one-month lead time of the previous run showed further improvement compared to CTL and BAS. Skill scores of the tercile probability forecasts were found to exceed 0.3 for most of the target months.

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