In 2018, South Korea recorded its hottest summer since 1973, with 31 heat-wave days. Heatwaves with maximum temperatures above 33 degrees Celsius usually occur after the rainy season in summer. In recent years, not only has the frequency of heatwaves increased, but also their intensity. Summer in South Korea Summer in South Korea (from June to August) is usually hot and humid with a lot of rainfall during the rainy season of the East Asian monsoon (Changma). About 60 percent of precipitation falls during this season. The average temperature in summer was around 24.7 degrees Celsius in 2023. The amount of precipitation in summer that year stood at over 1,000 millimeters, more than four times higher than in winter. Climate change South Korea is known for its four distinct seasons, yet weather patterns have increasingly changed in recent decades, resulting in longer summers and shorter winters. This shows that South Korea is not excluded from the effects of climate change. Changing climate patterns in recent decades have also led to an intensification of precipitation and more heat waves in South Korea. Meanwhile, climate change is taken very seriously by South Koreans: about 48 percent of respondents to a 2019 survey said that global warming or climate change is the most important environmental issue for South Korea.

In 2023, the average summer temperature in South Korea was around **** degrees Celsius, up from **** degrees Celsius in the previous year. The highest temperature since 2000 was **** degrees Celsius in 2018, while the lowest temperature was **** degrees Celsius in 2003.

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

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.

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.

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.

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

Impacts of high air temperature of summer in Korea.

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

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

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.

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.

Climate data and weather trends for Jeonju, 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.

A service that provides weather forecasts (ultra-short-term forecasts/1-hour intervals, short-term forecasts/3-hour intervals) for beaches nationwide, tide information, wave height information (data from the closest marine observation equipment point to the beach), sunrise and sunset information (based on the point where the beach is located, using data from the Korea Astronomy and Space Science Institute), water temperature information, and high and low tides (based on the closest tide station to the beach, using data from the National Oceanographic Research Institute). To support safe leisure activities for the public during the summer, a weather service is provided for major beaches nationwide, and the target locations are Eulwang-ri, Wangsan, Hanagae, Minmeoru, Janggyeong-ri, Ongam, Sugi, Dongmak, Seopo-ri, Siplipo, Gureop, Ttepuru, Batjireum, Handeul, Keunpulan, Janggol, and Beolan, approximately 330 locations.

Temperature Forecast Project using ML

Problem Statement:

Data Set Information:

This data is for the purpose of bias correction of next-day maximum and minimum air temperatures forecast of the LDAPS model operated by the Korea Meteorological Administration over Seoul, South Korea. This data consists of summer data from 2013 to 2017. The input data is largely composed of the LDAPS model's next-day forecast data, in-situ maximum and minimum temperatures of present-day, and geographic auxiliary variables. There are two outputs (i.e. next-day maximum and minimum air temperatures) in this data. Hindcast validation was conducted for the period from 2015 to 2017.

Attribute Information:

For more information, read [Cho et al, 2020]. 1. station - used weather station number: 1 to 25 2. Date - Present day: yyyy-mm-dd ('2013-06-30' to '2017-08-30') 3. Present_Tmax - Maximum air temperature between 0 and 21 h on the present day (°C): 20 to 37.6 4. Present_Tmin - Minimum air temperature between 0 and 21 h on the present day (°C): 11.3 to 29.9 5. LDAPS_RHmin - LDAPS model forecast of next-day minimum relative humidity (%): 19.8 to 98.5 6. LDAPS_RHmax - LDAPS model forecast of next-day maximum relative humidity (%): 58.9 to 100 7. LDAPS_Tmax_lapse - LDAPS model forecast of next-day maximum air temperature applied lapse rate (°C): 17.6 to 38.5 8. LDAPS_Tmin_lapse - LDAPS model forecast of next-day minimum air temperature applied lapse rate (°C): 14.3 to 29.6 9. LDAPS_WS - LDAPS model forecast of next-day average wind speed (m/s): 2.9 to 21.9 10. LDAPS_LH - LDAPS model forecast of next-day average latent heat flux (W/m2): -13.6 to 213.4 11. LDAPS_CC1 - LDAPS model forecast of next-day 1st 6-hour split average cloud cover (0-5 h) (%): 0 to 0.97 12. LDAPS_CC2 - LDAPS model forecast of next-day 2nd 6-hour split average cloud cover (6-11 h) (%): 0 to 0.97 13. LDAPS_CC3 - LDAPS model forecast of next-day 3rd 6-hour split average cloud cover (12-17 h) (%): 0 to 0.98 14. LDAPS_CC4 - LDAPS model forecast of next-day 4th 6-hour split average cloud cover (18-23 h) (%): 0 to 0.97 15. LDAPS_PPT1 - LDAPS model forecast of next-day 1st 6-hour split average precipitation (0-5 h) (%): 0 to 23.7 16. LDAPS_PPT2 - LDAPS model forecast of next-day 2nd 6-hour split average precipitation (6-11 h) (%): 0 to 21.6 17. LDAPS_PPT3 - LDAPS model forecast of next-day 3rd 6-hour split average precipitation (12-17 h) (%): 0 to 15.8 18. LDAPS_PPT4 - LDAPS model forecast of next-day 4th 6-hour split average precipitation (18-23 h) (%): 0 to 16.7 19. lat - Latitude (°): 37.456 to 37.645 20. lon - Longitude (°): 126.826 to 127.135 21. DEM - Elevation (m): 12.4 to 212.3 22. Slope - Slope (°): 0.1 to 5.2 23. Solar radiation - Daily incoming solar radiation (wh/m2): 4329.5 to 5992.9 24. Next_Tmax - The next-day maximum air temperature (°C): 17.4 to 38.9 25. Next_Tmin - The next-day minimum air temperature (°C): 11.3 to 29.8T

Please note that there are two target variables here:

1) Next_Tmax: Next day maximum temperature

2) Next_Tmin: Next day minimum temperature

In May 2025, the average temperature in Seoul, South Korea was **** degrees Celsius. August 2024 was the hottest month in the city in the past six years, while December 2022 was the coldest, with an average temperature of minus *** degrees Celsius.

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.

Data Set Information:

This data is for the purpose of bias correction of next-day maximum and minimum air temperatures forecast of the LDAPS model operated by the Korea Meteorological Administration over Seoul, South Korea. This data consists of summer data from 2013 to 2017. The input data is largely composed of the LDAPS model's next-day forecast data, in-situ maximum and minimum temperatures of present-day, and geographic auxiliary variables. There are two outputs (i.e. next-day maximum and minimum air temperatures) in this data. Hindcast validation was conducted for the period from 2015 to 2017.

Attribute Information:

For more information, read [Cho et al, 2020]. 1. station - used weather station number: 1 to 25 2. Date - Present day: yyyy-mm-dd ('2013-06-30' to '2017-08-30') 3. Present_Tmax - Maximum air temperature between 0 and 21 h on the present day (°C): 20 to 37.6 4. Present_Tmin - Minimum air temperature between 0 and 21 h on the present day (°C): 11.3 to 29.9 5. LDAPS_RHmin - LDAPS model forecast of next-day minimum relative humidity (%): 19.8 to 98.5 6. LDAPS_RHmax - LDAPS model forecast of next-day maximum relative humidity (%): 58.9 to 100 7. LDAPS_Tmax_lapse - LDAPS model forecast of next-day maximum air temperature applied lapse rate (°C): 17.6 to 38.5 8. LDAPS_Tmin_lapse - LDAPS model forecast of next-day minimum air temperature applied lapse rate (°C): 14.3 to 29.6 9. LDAPS_WS - LDAPS model forecast of next-day average wind speed (m/s): 2.9 to 21.9 10. LDAPS_LH - LDAPS model forecast of next-day average latent heat flux (W/m2): -13.6 to 213.4 11. LDAPS_CC1 - LDAPS model forecast of next-day 1st 6-hour split average cloud cover (0-5 h) (%): 0 to 0.97 12. LDAPS_CC2 - LDAPS model forecast of next-day 2nd 6-hour split average cloud cover (6-11 h) (%): 0 to 0.97 13. LDAPS_CC3 - LDAPS model forecast of next-day 3rd 6-hour split average cloud cover (12-17 h) (%): 0 to 0.98 14. LDAPS_CC4 - LDAPS model forecast of next-day 4th 6-hour split average cloud cover (18-23 h) (%): 0 to 0.97 15. LDAPS_PPT1 - LDAPS model forecast of next-day 1st 6-hour split average precipitation (0-5 h) (%): 0 to 23.7 16. LDAPS_PPT2 - LDAPS model forecast of next-day 2nd 6-hour split average precipitation (6-11 h) (%): 0 to 21.6 17. LDAPS_PPT3 - LDAPS model forecast of next-day 3rd 6-hour split average precipitation (12-17 h) (%): 0 to 15.8 18. LDAPS_PPT4 - LDAPS model forecast of next-day 4th 6-hour split average precipitation (18-23 h) (%): 0 to 16.7 19. lat - Latitude (°): 37.456 to 37.645 20. lon - Longitude (°): 126.826 to 127.135 21. DEM - Elevation (m): 12.4 to 212.3 22. Slope - Slope (°): 0.1 to 5.2 23. Solar radiation - Daily incoming solar radiation (wh/m2): 4329.5 to 5992.9 24. Next_Tmax - The next-day maximum air temperature (°C): 17.4 to 38.9 25. Next_Tmin - The next-day minimum air temperature (°C): 11.3 to 29.8