In May 2025, the average temperature in Gwangju, South Korea was 18.2 degrees Celsius. August 2024 was the city's hottest month in the past six years, while December 2022 and February 2025 were the coldest, with an average temperature of 1.1 degrees Celsius.

In 2024, precipitation in Jeju in South Korea was the highest nationwide, with about 1928.9 millimeters. Gyeongnam followed with around 1713.6 millimeters.

The average temperature in South Korea in 2024 was **** degrees Celsius. The average temperature in South Korea has risen steadily over the years, which is shown in the graph. Extreme weather South Korea has a distinct four-season climate. Generally, summer in South Korea is humid and hot, while winter is dry and cold. However, the summer climate, which usually lasts from June to August, is getting longer and can last from May through to September. Especially in summer, extreme weather such as tropical nights, typhoons, and heatwaves occur. Recently, there was an increase in the consecutive days in which heatwaves reached temperatures above ** degrees. Greenhouse gas emissions South Korea is suffering from air pollution problems, such as yellow dust and fine dust, that have increased rapidly over recent years. In addition, as the carbon dioxide concentration has continued to rise, the average annual temperature has also risen steadily, resulting in abnormal climates, such as heatwaves in summer or extreme cold in winter. South Korea is one of the countries that produces a lot of greenhouse gases. Due to the manufacturing-oriented industrial structure, greenhouse gas emissions from energy use account for a large portion.

In June 2025, the average temperature in South Korea was **** degrees Celsius. August 2024 was the hottest month in the past five years, with a mean of around **** degrees Celsius. In the same period, December 2022 was the coldest month, with an average temperature of minus *** degrees Celsius.

Dataset Overview:

This dataset provides a detailed record of daily weather conditions in Seoul, South Korea, from January 1, 2024. The dataset is updated frequently, with new data added daily or every two days, making it a valuable resource for analyzing recent and historical weather patterns.

Contents:

The dataset contains the following columns: - Date (datetime): The date of the recorded weather data. - Maximum Temperature (tempmax): The highest temperature recorded on the day (°F). - Minimum Temperature (tempmin): The lowest temperature recorded on the day (°F). - Average Temperature (temp): The average temperature recorded on the day (°F). - Feels Like Temperature (feelslike): The perceived temperature, factoring in humidity and wind (°F). - Dew Point (dew): The temperature at which dew forms (°F). - Humidity (humidity): The percentage of humidity in the air. - Precipitation (precip): The total precipitation recorded (mm). - Snow (snow): The total snowfall recorded (mm). - Wind Speed (windspeed): The average wind speed (km/h). - Wind Direction (winddir): The direction from which the wind is blowing (degrees). - Sea Level Pressure (sealevelpressure): The atmospheric pressure at sea level (hPa). - Cloud Cover (cloudcover): The percentage of sky covered by clouds. - Visibility (visibility): The visibility distance (km). - Solar Radiation (solarradiation): The solar radiation received on the surface (W/m²). - UV Index (uvindex): The UV index measuring the strength of sunburn-producing ultraviolet radiation. - Conditions (conditions): A description of the weather conditions (e.g., Clear, Partly Cloudy). - Description (description): A textual description of the day's weather.

Usage and Applications:

• Weather Analysis: Analyze trends and patterns in weather conditions over time.
• Climate Studies: Investigate changes in climate variables and their potential impacts.
• Machine Learning: Train models for weather prediction, anomaly detection, or related tasks.
• Comparative Analysis: Compare weather data with other regions or historical data for Seoul.

Data Source:

The data is sourced from reliable meteorological stations and compiled by [Your Data Source or Provider]. The dataset is continuously updated to provide the latest available data.

Updates:

This dataset is actively maintained and updated daily or every two days, ensuring that it reflects the most current weather conditions. Please check back regularly for the latest updates.

**Note: **This dataset is intended for educational and research purposes. Users are encouraged to cite the original data source when using this dataset in publications or presentations.

For extreme temperature, we used climate extreme indices provided by CLIVAR (Climate and Ocean-Variability, Predictability, and Change) ETCCDI (Expert Team on Climate Change Detection and Indices). ETCCDI has provided 27 climate extreme indices not only with global reanalysis datasets but with CMIP5 simulations. The indices data are available on-line and the results with CMIP5 simulations were summarized by Sillmann et al. [2013]. For our analysis, we downloaded a monthly minimum of daily minimum surface air temperature (TNn) and a monthly maximum of daily maximum temperature (TXx). Among the CMIP5, 27 model results available on their website, we used 23 model results containing both of the TNn and TXx for all of the historical, RCP 4.5 and 8.5 experiments.

Since our focus is on boreal-winter extreme temperature, we selected the lowest TNn and highest TXx among the three months of December-January-February every year from 1861 to 2005 for the historical simulation and from 2006 to 2099 for the RCP 4.5 and RCP 8.5 scenario. Before the spatial averaging over the analysis domain (34°N-43°N in latitude and 124°E-131°E in longitude including the Korean Peninsula), we had remapped all of the simulation data onto a 1.5° x 1.5° horizontal resolution.

The time of unprecedented climate (TUC) for extreme temperature is defined in this study as the beginning year when the extreme temperature projected for the future climate scenarios exceed a critical value in all subsequent years during the RCP scenario runs.

In this study, the critical value for extreme temperatures is specified as a 50-year return level which is rather arbitrary but refers to a rough estimate for the social lifetime of a man. One may find the return level empirically from historical data, but this study estimates it using a Generalized Extreme Value distribution function as suggested by Kharin et al. [2007]. Based on the CMIP5 historical simulation data using R, we obtained three parameters determining a GEV distribution for each model, respectively for TNn and TXx. The GEV distribution for each model and variable has been verified using a Q-Q (quantile-quantile) plot if it adequately describes the CMIP5 historical data. All of the models showed the Q-Q plot within the 95% confidence range (Figure 1a for GFDL-ESM2G TXx for an instance). Then, we estimated the return level from the distribution and TUC from the RCP scenario runs for the wintertime TNn and TXx averaged over Korea.

The daily minimum temperature record and the monthly heat sales record

Climate data and weather trends for Hwaseong-si, South Korea. View temperature patterns, precipitation data, and historical climate analysis.

Climate data and weather trends for Changwon, South Korea. View temperature patterns, precipitation data, and historical climate analysis.

In 2024, the average maximum temperature in South Korea reached **** degrees Celsius, slightly higher than the previous year. The annual average maximum temperature in South Korea has risen steadily over the measured period. Temperature trends by season South Korea has four seasons, each characterized by its own distinctive temperature trends. The average summer temperature recorded in South Korea has ranged from ** to ** degrees Celsius. Although average temperatures generally indicate moderate warmth, 31 heat-wave days were recorded in 2018 alone, far above the average value. Conversely, winter in South Korea is the coldest and driest season, with an average temperature of about *** degrees Celsius in 2023. Climate change and response Climate change has impacted South Korea. Despite the minor ups and downs in temperature, the annual average temperature has moved gradually upward, showing a difference of more than *** degree Celsius from 2023 to 1973. Additionally, the number of heatwave days has increased substantially compared to previous decades. This has not gone unnoticed, as most legislative members of the National Assembly have identified addressing the enactment and revision of policies as a priority in responding to the climate crisis.

Climate data and weather trends for Ansan-si, South Korea. View temperature patterns, precipitation data, and historical climate analysis.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Borehole. The data include parameters of borehole with a geographic location of South Korea, Eastern Asia. The time period coverage is from 450 to -43 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Köppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC).

Korea is one of the countries that consume natural gas most in the world to heat houses.

Gas demand is dependent on the weather; ascending demand by getting colder.

This data can help anticipate future gas demand.

Understanding shifts in autumn phenology associated with climate changes is critical for preserving forest ecosystems. This study examines the changes in the leaf coloring date (LCD) of two temperate deciduous tree species, Acer palmatum (Acer) and Ginkgo biloba (Ginkgo), in response to surface air temperature (Ts) changes at 54 stations of South Korea for the period 1989–2007. The variations of Acer and Ginkgo in South Korea are very similar: they show the same mean LCD of 295th day of the year and delays of about 0.45 days year-1 during the observation period. The delaying trend is closely correlated (correlation coefficient > 0.77) with increases in Ts in mid-autumn by 2.8 days °C-1. It is noted that the LCD delaying and temperature sensitivity (days °C-1) for both tree species show negligible dependences on latitudes and elevations. Given the significant LCD-Ts relation, we project LCD changes for 2016–35 and 2046–65 using a process-based model forced by temperature from climate model simulation. The projections indicate that the mean LCD would be further delayed by 3.2 (3.7) days in 2016–35 (2046–65) due to mid-autumn Ts increases. This study suggests that the mid-autumn warming is largely responsible for the observed LCD changes in South Korea and will intensify the delaying trends in the future.

The GPM Ground Validation SEA FLUX ICE POP dataset includes estimates of ocean surface latent and sensible heat fluxes, 10m wind speed, 10m air temperature, 10m air humidity, and skin sea surface temperature in support of the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP) field campaign in South Korea. The two major objectives of ICE-POP were to study severe winter weather events in regions of complex terrain and improve the short-term forecasting of such events. These data contributed to the Global Precipitation Measurement mission Ground Validation (GPM GV) campaign efforts to improve satellite estimates of orographic winter precipitation. This data file is available in netCDF-4 format from September 1, 2017 through April 30, 2018.

Climate data and weather trends for Bucheon-si, South Korea. View temperature patterns, precipitation data, and historical climate analysis.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Borehole. The data include parameters of borehole with a geographic location of South Korea, Eastern Asia. The time period coverage is from 450 to -39 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Understanding climatic effect on wildlife is essential to prediction and management of climate change’s impact on the ecosystem. The climatic effect can interact with other environmental factors. This study aimed to determine effects of climate and altitude on Siberian roe deer (Capreolus pygargus) activity in temperate forests of South Korea. We conducted camera trapping to investigate roe deer’s activity level from spring to fall. Logistic regressions were used to determine effects of diel period, temperature, rain, and altitude on the activity level. A negative relationship was noted between temperature and the activity level due to thermoregulatory costs. Roe deer activity exhibited nocturnal and crepuscular patterns during summer and the other seasons, respectively, possibly due to heat stress in summer. In addition, the effect of temperature differed between high- and low-altitude areas. In low-altitude areas, temperature affected negatively the activity level throughout the study..., The camera trapping method was used to observe temporal variations in roe deer capture (sampling days: September to October 2021 and April to August 2022). In the study area, a 5 × 6 grid design (interval = 600 m) was established, and one trail camera (Spec Ops Elite HP4; Browning Co., USA) was deployed corresponding to each cell of the grid. The study period was divided into five seasons, and further analyses were performed for each season: spring (15 April to 15 May, 960 trap-days), early summer (16 May to 30 June, 1380 trap-days), summer (1 July to 31 August, 1860 trap-days), early fall (September, 900 trap-days) and fall (October, 810 trap-days). The camera-plot altitudes were categorised into four classes: 600 (600–800 m asl, n = 3), 800 (800–1,000 m asl, n = 10), 1,000 (1,000–1,200 m asl, n = 11) and 1,200 (1,200–1,400 m asl, n = 6). We created a roedeer variable as presence/absence of observation per 2-h in each altitude class. In order to account for sampling effort depending on..., , This README file was generated on 2023-09-22 by Tae-Kyung Eom.

GENERAL INFORMATION

1. Title of Dataset: Adaptive response of Siberian roe deer (Capreolus pygargus) to climate and altitude in the temperate forests of South Korea

2. Author Information A. Principal Investigator Contact Information Name: Tae-Kyung Eom Institution: Chung-Ang University Address: Ansung, South Korea Email: xorud147@naver.com

   B. Associate or Co-investigator Contact Information Name: Jae-Kang Lee Institution: Chung-Ang University Address: Ansung, South Korea

   Name: Dong-Ho Lee Institution: Chung-Ang University Address: Ansung, South Korea

   Name: Hyeongyu Ko Institution: Chung-Ang University Address: Ansung, South Korea

   Name: Shin-Jae Rhim Institution: Chung-Ang University Address: Ansung, South Korea

3. Date of data collection (single date, range, approximate date): 2021-2022

4. Geographic location of data collection: Mt. Gariwang, Pyeo...

In 2023, the average temperature for summer in South Korea was **** degrees Celsius. South Korea has four distinct seasons, which can be seen in the different average temperatures for each season.