Made August 2023/Updated OCT 2023/MAR 2025/AUG 2026
The purpose of this post is merely to catalogue counts of monthly record reports (high maximums, high minimums, low minimums, low maximums) coming into the National Center for Environmental Information’s site and all related charts and graphs produced in my Excel files for data sets from Sweden. A monthly record report would be one for the most extreme temperature reading reported during one individual month during a calendar year. Each individual station would only have 12 monthly records for high maximums (only one per month), for example.
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 2000-2009. 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.”
All counts include ties of records.
An updated 2016 study from Dr. Jerry Meehl indicates that the ratio from year to year will average around 15 to 1 by 2100 for the United States:
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. So far, the ratio of monthly record high maxes to low minimums for the 2020s is higher than any other decade since the 1910s:
Here are the current monthly record counts per decade, which have gone into the prior chart:
New this update: Swedish Climate-Sensitivity Findings
Our Swedish analysis found a strong and remarkably consistent relationship between temperature anomaly and the balance of warm versus cold temperature records across all four datasets, using 1961–1990 as the anomaly baseline.
- DHMX/DLMN — Daily High Maximums vs. Daily Low Minimums: Cold anomalies strongly favor record lows, while increasingly warm anomalies rapidly shift the balance toward record highs.
- DHMN/DLMX — Daily High Minimums vs. Daily Low Maximums: The same temperature dependence appears in the complementary daily-record measures, providing an independent confirmation that the signal is not confined to daytime maximum temperatures.
- MHMX/MLMN — Monthly High Maximums vs. Monthly Low Minimums: The relationship remains very strong when individual daily extremes are replaced by monthly records. Warm monthly anomalies strongly favor new warm records, while cold anomalies favor cold records.
- MHMN/MLMX — Monthly High Minimums vs. Monthly Low Maximums: The fourth and complementary monthly dataset again produces the same fundamental response, completing the pattern across both maximum and minimum temperature extremes.
Overall conclusion: Sweden shows a coherent climate-sensitivity signal across all four independent record measures. As monthly temperature anomalies move from cold to warm, the record balance systematically shifts from cold-record dominance to warm-record dominance. The fact that this relationship appears in daily and monthly records and in both maximum- and minimum-temperature measures makes the result substantially stronger than any one dataset alone.
In short, Swedish extreme-temperature records respond strongly and consistently to relatively small changes in mean temperature anomaly, providing another national example of the nonlinear amplification of temperature extremes as the climate warms.
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. This year I found a good source for temperature anomalies and averages:
For the Swedish temperature averages and anomalies, the underlying temperature source we used was the CRU (Climatic Research Unit, University of East Anglia) national-average temperature dataset, distributed through ClimGen/CRU and also used by the World Bank Climate Change Knowledge Portal.
Specifically:
- CRU TS observational temperature data provided the Swedish national monthly mean temperatures. The CRU Sweden page gives national-average monthly temperatures and the 1961–1990 climatological means.
- We expressed each month’s temperature as an anomaly from its corresponding 1961–1990 monthly average, so January is compared with the 1961–1990 January mean, February with February, etc.
- SMHI (Swedish Meteorological and Hydrological Institute) provides the authoritative Swedish confirmation of the 1961–1990 reference-normal period. SMHI explicitly maintains 1961–1990 as the reference period for climate-change studies and publishes Swedish monthly temperature normals for it.
- The World Bank Climate Change Knowledge Portal independently documents that its observed historical temperature product is derived from CRU TS, with monthly data and 30-year climatologies including 1961–1990.
So for your methodology/source note, I would state it simply as:
Swedish monthly mean temperatures and temperature anomalies: Climatic Research Unit (CRU), University of East Anglia, CRU TS observational dataset. Temperature anomalies are referenced to the 1961–1990 monthly climatological averages. The 1961–1990 reference period is consistent with SMHI/WMO climate-reference standards.
CRU/ClimGen Sweden temperature data
SMHI 1961–1990 Swedish climate normals
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 charts of counts of monthly high minimums and low maximums 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 1910 since record counts decrease substantially prior to the year 1912. Average temperature ranking slots are left blank. 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 (so far) is higher than any other decade since the 1910s:
Here are the current monthly record counts per decade:
New this update: Swedish Climate-Sensitivity Findings
Our Swedish analysis found a strong and remarkably consistent relationship between temperature anomaly and the balance of warm versus cold temperature records across all four datasets, using 1961–1990 as the anomaly baseline.
- DHMX/DLMN — Daily High Maximums vs. Daily Low Minimums: Cold anomalies strongly favor record lows, while increasingly warm anomalies rapidly shift the balance toward record highs.
- DHMN/DLMX — Daily High Minimums vs. Daily Low Maximums: The same temperature dependence appears in the complementary daily-record measures, providing an independent confirmation that the signal is not confined to daytime maximum temperatures.
- MHMX/MLMN — Monthly High Maximums vs. Monthly Low Minimums: The relationship remains very strong when individual daily extremes are replaced by monthly records. Warm monthly anomalies strongly favor new warm records, while cold anomalies favor cold records.
- MHMN/MLMX — Monthly High Minimums vs. Monthly Low Maximums: The fourth and complementary monthly dataset again produces the same fundamental response, completing the pattern across both maximum and minimum temperature extremes.
Overall conclusion: Sweden shows a coherent climate-sensitivity signal across all four independent record measures. As monthly temperature anomalies move from cold to warm, the record balance systematically shifts from cold-record dominance to warm-record dominance. The fact that this relationship appears in daily and monthly records and in both maximum- and minimum-temperature measures makes the result substantially stronger than any one dataset alone.
In short, Swedish extreme-temperature records respond strongly and consistently to relatively small changes in mean temperature anomaly, providing another national example of the nonlinear amplification of temperature extremes as the climate warms.
For the Swedish temperature averages and anomalies, the underlying temperature source we used was the CRU (Climatic Research Unit, University of East Anglia) national-average temperature dataset, distributed through ClimGen/CRU and also used by the World Bank Climate Change Knowledge Portal.
Specifically:
- CRU TS observational temperature data provided the Swedish national monthly mean temperatures. The CRU Sweden page gives national-average monthly temperatures and the 1961–1990 climatological means.
- We expressed each month’s temperature as an anomaly from its corresponding 1961–1990 monthly average, so January is compared with the 1961–1990 January mean, February with February, etc.
- SMHI (Swedish Meteorological and Hydrological Institute) provides the authoritative Swedish confirmation of the 1961–1990 reference-normal period. SMHI explicitly maintains 1961–1990 as the reference period for climate-change studies and publishes Swedish monthly temperature normals for it.
- The World Bank Climate Change Knowledge Portal independently documents that its observed historical temperature product is derived from CRU TS, with monthly data and 30-year climatologies including 1961–1990.
So for your methodology/source note, I would state it simply as:
Swedish monthly mean temperatures and temperature anomalies: Climatic Research Unit (CRU), University of East Anglia, CRU TS observational dataset. Temperature anomalies are referenced to the 1961–1990 monthly climatological averages. The 1961–1990 reference period is consistent with SMHI/WMO climate-reference standards.
CRU/ClimGen Sweden temperature data
SMHI 1961–1990 Swedish climate normals
The 2020s through July 2026:
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 monthly record count data for the Sweden in the NCEI database back to 1910.
Guy Walton…”The Climate Guy”