For the comprehensive estimation of regional climate change over East Asia (EA) at the 2 and 3 °C global warming levels (GWLs), the Köppen‐Trewartha climate‐type change is assessed with ensemble regional climate change projections in line with Coordinated Regional Climate Downscaling Experiment (CORDEX)‐EA phase 2. Under the 2 °C (3 °C) GWL, 17.6% (25.2%) of the EA region is expected to undergo major climate‐type changes. Tropical and subtropical climate types will expand northward, accompanied by increasing hydroclimatic intensity. Limiting GWL to 2 °C shows benefits by preventing subtropical‐type expansion around far EA. Desertification over inland regions of EA exhibits scenario dependency. Boreal and tundra climate types over high‐latitude regions will tend to decrease rapidly, especially over the Tibetan Plateau. The results are expected to be a baseline assessment of climate change over EA under the 2 and 3 °C GWLs above the preindustrial level.

A new model output statistics method – Ensemble Selective Simple Linear Regression (E-SSLR) – is developed based on SLR in order to increase the seasonal prediction skill of a Coupled General Circulation Model (CGCM) over the Maritime Continent (MC), a region with large model simulation errors. E-SSLR is applied to Pusan National University (PNU) CGCM hindcast over the MC region for the period of 1981–2010 to reduce the systematic model bias in boreal winter (DJF) seasonal mean precipitation and outgoing long-wave radiation (OLR) anomalies. Three oceanic indices (Nino 3.4, El Nino Modoki and Indian Ocean Dipole (IOD) Mode indices) and one atmospheric index (Southern Oscillation Index, SOI) produced from PNU CGCM hindcast are used as SLR predictor. E-SSLR consists of three steps: Selection, SLR and Ensemble. The selection and ensemble steps are added to the conventional SLR step to overcome the weakness of the linear regression method. In the selection step, the grids with a temporal correlation coefficient between predictor and predictand exceeding the threshold are selected. These grids (grid-selected) are corrected by SLR in the second step. For the grids that are grid-not-selected, the original CGCM results are used without further correction. This prevents insignificant statistical correction due to the application of low correlated predictors to the SLR. The correction effect of E-SSLR is analysed in terms of deterministic and categorical analyses. The result shows that the seasonal predictability of DJF seasonal precipitation and OLR in the MC region is increased by using E-SSLR, and this increment is statistically significant. The correction effect is larger when indices with high predictability that are closely correlated with the predictand are used as predictors.

The changes of agro-climate and heat extremes, and their impact on rice cultivation are assessed over South Korea in context of 2 and 3 °C global warming levels (GWL) compared to pre-industrial levels, with ensemble regional climate model projection produced under the Coordinated Regional Climate Downscaling Experiment–East Asia (CORDEX-EA) phase 2 protocols. It is found that the mean temperature increase under global warming has not only positive effects such as the extension of vegetable and crop periods and the widening of the cultivatable regions but also negative effects due to the shortening of the reproductive growth period. On the other hand, extreme heat changes in the future clearly show a negative effect on rice cultivation via the increase of hot days during heat-sensitive stages (27.16% under 2 °C GWL, 54.59% under 3 °C GWL) among rice phenology which determines the rice yield in tandem with rice flowering, ripening, and sterility problems. The major type of heat extreme is dominated by nationwide warm anomalies covering entire S. Korea, and the proportion of this type is projected to increase from 35.8% to 49.5% (57.4%) under 2 °C (3 °C) GWL in association with the thermal expansion of atmosphere which links to the favorable environment for occurring barotropic anti-cyclonic system.

Unlike natural ecosystems, agricultural ecosystems are unique ecosystems in which artificial factors play a significant role. The material cycling within an agricultural ecosystem is influenced by factors such as agricultural activities, weather, and soil conditions. Understanding the material cycling and energy flow in these ecosystems is important to cope with climate change. In this study, we measured energy and carbon dioxide flux using the eddy covariance method to assess material cycling in rice paddy and soybean field ecosystems with similar weather conditions but different vegetation. Additionally, growth surveys were conducted every two weeks to analyze crop development. During the summer, the weather and soil conditions in rice paddy and soybean field were comparable, resulting in similar levels of latent heat flux for both ecosystems. In July 2020, despite the rainy season, the water use efficiency(WUE) of rice paddy was higher than that of other periods, influenced by vegetation and weather conditions. WUE during the summer resembled that of the cropping period, indicating a potential impact on overall crop grain weight.