Made January 2022/Updated FEB 2025/JUN 2026
The purpose of this post is merely to catalogue counts of monthly record high maximums, low minimums, high minimums and low, maximums coming into the National Center for Environmental Information’s site and all related charts and graphs produced in my Excel files for Russian data sets. Monthly records are those set for the entire period of one month. For example, the highest temperature set during the month of March at Atlanta Hartsville-Jackson Airport was 89F set on 3/23/1995. I am in the process of constantly updating this data verifying the 2009 Meehl et. all surface Records published in geophysical Science that I initiated from 2009. Each individual count could be a tied surface record or one broken by several degrees Fahrenheit.
Here is the link to the NCEI site:
https://www.ncdc.noaa.gov/cdo-web/datatools/records
More from NCEI:
“The daily records summarized here are compiled from a subset of stations in the Global Historical Climatological Network. A station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset.
For a station to be considered for any parameter, it must have a minimum of 30 years of data with more than 182 days complete each year. This is effectively a “30-year record of service” requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not “thread” the separate histories into one record for a city.
This tool provides simplistic counts of records to provide insight into recent climate behavior, but is not a definitive way to identify trends in the number of records set over time. This is particularly true outside the United States, where the number of records may be strongly influenced by station density from country to country and from year to year. These data are raw and have not been assessed for the effects of changing station instrumentation and time of observation.”
An updated 2016 study from Dr. Jerry Meehl indicates that the daily ratio from year to year will average around 15 to 1 by 2100:
Per one of the authors of both the 2009 and 2016 studies, Claudia Tebaldi said “This climate is on a trajectory that goes somewhere we’ve never been. And records are a very easy measure of that.”
All of the data listed below is part of this one chart. The ratio of monthly record high maxes to low minimums for the 2020s, so far, is higher than any decade since the 1910s:
Here are current Russian monthly record counts per decade (including ties):
A short summary of the MHMX/MLMN anomaly findings from 2026 is:
Monthly all-time warm maximum records (MHMX) become increasingly dominant during positive temperature anomaly periods, while monthly all-time cold minimum records (MLMN) dominate during negative anomaly periods. The transition from cold-record dominance to warm-record dominance occurs rapidly near the climatological baseline, demonstrating that even modest warming substantially increases the likelihood of setting all-time warm records.
More specifically:
- Negative anomaly bins are dominated by MLMN records.
- Positive anomaly bins are dominated by MHMX records.
- The warm-record advantage increases nonlinearly as anomalies become more positive.
- The relationship persists despite the rarity of all-time records, indicating that the signal is systematic rather than random.
- The transformed-ratio curves show that background temperature anomalies are a strong predictor of all-time monthly record behavior.
For Russia and other regions you have examined, the MHMX/MLMN results indicate that:
A warming climate strongly favors the establishment of new all-time monthly warm maxima while suppressing the occurrence of all-time monthly cold minima, with the effect becoming increasingly pronounced as temperature anomalies rise.
Scientifically, the MHMX/MLMN metric provides a particularly sensitive indicator of long-term climate warming because it examines the rarest and most extreme monthly temperature records.
Here are Russian monthly temperature anomalies vs. record ratios. Note the number of months in each bin:
Formula Notes
R = DHMX / DLMN
If R >= 1, then T = R
If R < 1, then T = -(1/R)
The 2020s:
Blue colors represent cold months and red warm. Those months with counts close to a 1 to 1 ratio of highs to lows are colored black. I have opted not to catalogue data prior to 1900 since record counts decrease substantially prior to the decade of the 1900s. I am looking for Russian national temperature rankings for all months and years. Drop me a line if you have that information. Time stamps for when I last updated counts are located in the upper left-hand corner of each chart. Also, drop me a note if you see an error or if you have suggestions for improvements.
The 2010s:
The 2000s:
The 1990s:
The 1980s:
The 1970s:
The 1960s:
The 1950s:
The 1940s:
The 1930s:
The 1920s:
The 1910s:
For the following data sets of record Russian monthly high minimums and low maximums I have opted not to boldly highlight ratios greater than 10 to 1. Blue colors represent cold months and red warm. Those months with counts close to a 1 to 1 ratio of highs to lows are colored black. I have opted not to catalogue data prior to 1900 since record counts decrease substantially prior to the decade of the 1910s. Time stamps for when I last updated counts are located in the upper left-hand corner of each chart. Drop me a note if you see an error or if you have suggestions for improvements.
All of the data listed below is part of this one chart. The ratio of monthly record high minimums to low maximums for the 2020s are higher, so far, than any other decade since the 1910s:
Here are the current monthly record counts per decade:
Formula Notes
R = DHMX / DLMN
If R >= 1, then T = R
If R < 1, then T = -(1/R)
Based on our MHMN/MLMX anomaly-bin research, a parallel summary would be:
Monthly all-time warm minimum records (MHMN) become increasingly dominant during positive temperature anomaly periods, while monthly all-time cold maximum records (MLMX) dominate during negative anomaly periods. The transition from cold-record dominance to warm-record dominance occurs near the climatological baseline, indicating that even modest warming strongly favors unusually warm nights while suppressing unusually cool daytime conditions.
More specifically:
- Negative anomaly bins are dominated by MLMX records.
- Positive anomaly bins are dominated by MHMN records.
- The warm-record advantage increases rapidly and nonlinearly as anomalies become more positive.
- The transformed-ratio curves show that background temperature anomalies strongly control the occurrence of all-time minimum and maximum record events.
- The persistence of the relationship despite the small number of all-time records indicates that the signal is systematic and climatically driven rather than random.
Scientifically, the MHMN/MLMX metric is especially important because it captures changes in both:
- Nighttime warmth (MHMN), which is highly sensitive to greenhouse forcing, increased water vapor, cloud cover, and Arctic amplification; and
- Suppression of unusually cool daytime maxima (MLMX), which become increasingly rare in a warming climate.
A concise scientific conclusion would be:
A warming climate strongly favors the occurrence of all-time monthly warm minimum records while greatly reducing the frequency of all-time monthly cold maximum records, with the imbalance increasing as temperature anomalies rise. This relationship demonstrates that background warming is systematically shifting the most extreme monthly temperature records toward warmer conditions.
This finding complements the MHMX/MLMN results and reinforces the conclusion that both daytime and nighttime temperature extremes are responding strongly to long-term climate warming, with nighttime extremes generally exhibiting the greatest sensitivity.
The 2020s:
The 2010s:
The 2000s:
The 1990s:
The 1980s:
The 1970s:
The 1960s:
The 1950s:
The 1940s:
The 1930s:
The 1920s:
The 1910s:
This is all of the NCEI Russian monthly record count data going back to 1910.
Guy Walton…” The Climate Guy”