To investigate the recent effects of climate change in the upper Geum River basin in South Korea, a detailed trend analysis of 17 extreme climate indices based on 33 years (1988–2020) of daily precipitation, and daily (minimum and maximum) temperature data has been analyzed in this study. Out of the 17 extreme climate indices, nine (eight) indices were based on temperature (precipitation) data. Trend analysis based on detailed temporal scales (annual, seasonal, monthly) were performed through the Mann–Kendall trend test and the Theil–Sen slope method. Furthermore, the Mann–Whitney–Pettit test was also applied in this study, to detect abrupt changes in the extreme climate indices. Based on the results of this study, the climate conditions at the upper Geum River basin for the past three decades can be summarized as follows: general increase in temperature intensity, decrease in cold duration, increased heat duration, increased precipitation intensity, and increased consecutive wet and dry durations. Furthermore, a prolonged summer season (shorter spring, and autumn periods) and precipitation shifts, were detected based on trend analysis results of seasonal, and monthly time scales. The results presented in this study can provide supplementary data for improving watershed management strategies in the upper Geum River basin.

Megadroughts represent droughts with a high-intensity, long-lasting duration, and extensive spatial extent. However, as these characteristics have not been quantitatively proposed, a reasonable guideline for identifying megadroughts has not been determined. This study aims to identify the quantitative characteristics of a megadrought and suggest a guideline for detecting the occurrence of the megadrought. This study focused on the central Chile megadrought that occurred from 2010 to 2016 in central Chile. The megadrought was analyzed using the standardized precipitation index (SPI) while increasing its timescale. The 60-month timescale spatially represented that more than 60% of all stations in central Chile experienced a drought indicating moderate or more drought intensity (SPI ≤ −1) for a long duration of more than 36 months in the megadrought period. Then, guidelines were suggested based on the timescale, drought intensity, and spatial extent of drought as the criteria to detect the occurrence of the megadrought. In the criteria of moderate or more drought intensity and the spatial extent of 50% or 60%, the 60-month timescale detected the occurrence of the megadrought the earliest and most appropriately. This study will be used as a useful guideline to define and detect megadroughts that will occur in the future.

This study aimed to evaluate a probable maximum precipitation (PMP) estimation approach using a moisture‐maximization method. This method enables the estimation of precipitable water (PW) using a surface dew point, under a pseudoadiabatic assumption. However, the deviation of the PW (as estimated using the surface dew point) from the actual observed PW mostly leads to an uncertain PMP estimation, as it is difficult to estimate the actual atmospheric moisture content in an air column using the surface dew point alone. In this study, to consider abundant atmospheric data spanning a long‐term period, we employed the reanalysis data from “Japanese 55‐year Reanalysis” (JRA‐55). The data showed good reliability for the surface dew point and PW. To quantify the deviation, we estimated the errors between the actual and estimated PW values for the moisture‐maximization method at 30 points across Japan. Here, the actual PW was extracted directly from JRA‐55, and the estimated PW was obtained using the surface dew point extracted from JRA‐55. We confirmed that the event PW was the largest source of error in the PMP estimation and that the magnitude of the errors was highly correlated with the surface dew point. We analyzed the vertical dew point profile for events with large estimation errors and confirmed that a low surface dew point showed a high deviation in the air column. Therefore, the use of PMP estimations with the moisture‐maximization method should be carefully considered in rainfall events with low surface dew points (e.g., lower than 18°C).

This study aimed to evaluate probable maximum precipitation (PMP) estimated using surface dew points (SDP) or actual precipitable water obtained from upper‐air data (UAD) in the moisture‐maximization method with the help of sufficient extreme precipitation events using large‐scale climate ensemble simulation data (d4PDF). The deviations between the PMP variables estimated by the SDP and UAD approaches were analyzed for southern and northern areas of Japan to consider the regional characteristics of the deviations. We found that the deviations were high in northern areas where the SDPs are relatively low during precipitation events. The PMPs estimated using each approach were also compared to the extreme‐scale reference precipitation proposed in this study. The SDP approach overestimated the PMPs by over 20% compared to the reference precipitation in the northern region. However, the UAD approach showed very low average errors in all southern and northern areas. This tendency of the SDP approach was significantly related to the regional climatic characteristics of the SDP, which indicated that the SDP approach may estimate an uncertain PMP value depending on each regional climatic characteristic compared to the UAD approach. Regional climatic characteristics should be considered when using the SDP approach to estimate the PMP.

Probable maximum precipitation (PMP) is one of the most important key indices in hydrological design, and it should be estimated without the risk of overestimation or underestimation. However, observed extreme rainfall is insufficient to evaluate the estimated PMP in most regions, and there are no conclusive criteria to evaluate the possibility of PMP over- or underestimation. Our study aims to evaluate the reasonability of PMP estimation using a moisture-maximization method by comparing estimated PMPs based on the conventional method using surface dew points (SDP) with values based on actual precipitable water obtained from upper-air data (UAD). Furthermore, the estimated PMPs were evaluated with extreme-scale reference precipitation estimated from large ensemble climate simulation data (d4PDF) to check the possibility of PMP over- and underestimation. This reference value corresponds to a 3,000-year return period. The UAD-based estimation showed a reasonable PMP with a low deviation to the reference precipitation in most target areas, and the SDP-based estimation showed overestimated PMPs to the reference precipitation in areas with low SDPs. To control PMP overestimation of the SDP-based approach, the upper bound of the moisture maximizing ratio (MMR) in the SDP-based approach was limited to 2.0. Consequently, the SDP-based approach, which limits the upper bound of MMR, decreased deviations between estimated PMPs and reference values in areas with low SDPs. The SDP approach could reduce the possibility of PMP overestimation by limiting the upper bound of MMR. This evaluation could be conducted from extreme-scale reference precipitation under sufficient extreme events by utilizing the d4PDF.

This study aims to apply large-scale climate ensemble simulation data to evaluate the magnitude of extreme rainfall events. It was conducted on the basis of extreme rainfall that occurred in the Hiroshima region in 2018. This event recorded an extreme rainfall magnitude corresponding to a return period of 1,000 years at a 24-h rainfall duration; it is difficult to evaluate this magnitude with the concept of probability rainfall based on frequency analysis due to the short observation period. To overcome this limitation, the data for policy decision making for future (d4PDF) climate change database based on a large-scale climate ensemble simulation was used. These data provided 3,000 annual maximum daily rainfall values, which were used to empirically estimate the probability rainfall with a return period of 10-1,000 years based on a non-parametric approach without statistical methods. The estimated probability rainfall of the d4PDF was compared with the estimated probability obtained from the observed rainfall and frequency analysis. The difference between the two probability rainfall values was 3.53% for the return period of 50 years. However, as the return period increases, the error increases to more than 10%. This indicates that the estimation of the probability rainfall with a long-term return period using the observed data of a relatively short period may present uncertainties. Regarding the probability rainfall using d4PDF under present climate conditions, the Hiroshima event represented a return period of nearly 300 years. Meanwhile, for the probability rainfall using the d4PDF under future climate conditions, the Hiroshima event had a return period of nearly 100 years. Consequently, the magnitude of the probability rainfall increased in future climate conditions, and the probability of the occurrence of extreme rainfall corresponding to the Hiroshima event increased from 0.33% to 1%. Therefore, d4PDF can be used to quantitatively evaluate the magnitude of extreme rainfall events under present and future climate conditions.