Global temperature anomaly, monthly (GISTEMP)
Monthly global and hemispheric land-ocean temperature anomaly, 1880-01 onward (NASA GISTEMP v4, degrees C vs the 1951-1980 baseline): one row per (month, region) for Global, Northern Hemisphere and Southern Hemisphere, with the trailing-12-month rolling mean anomaly and a calendar-month warm-record flag. The catalog's only long-horizon climate-anomaly index — the weather datasets it sits beside (nasa_power, metro_thermal_demand_intel, noaa_nclimdiv_degree_days) are short-horizon nowcasting inputs. Who joins this: shops weight seasonal demand and energy-exposed categories by the anomaly regime; subscription businesses use the rolling mean as an exogenous churn/demand feature; sales teams read it as background context for climate-exposed territories.
- Rows
- 5,280
- Columns
- 8
- Source cadence
- Monthly
- Last refreshed
- Oct 1, 2026
- Theme
- climate
| Column | Type | Description |
|---|---|---|
| date | date | First day of the observation month. The panel join key (with region). (unit: date) |
| year_month | string | Calendar month as YYYY-MM. (unit: string) |
| region | string | GISTEMP region code: GLB (global), NH (Northern Hemisphere), SH (Southern Hemisphere). (unit: string) |
| region_label | string | Human label for the region code. (unit: string) |
| anomaly_c | float | Land-ocean temperature anomaly in degrees Celsius vs the 1951-1980 baseline (GISTEMP v4 Ts+dSST tables). (unit: degrees Celsius) |
| rolling_12m_mean_c | float | Trailing-12-month rolling mean of the anomaly within the region (null until 12 observations). (unit: degrees Celsius) |
| record_warm_month_flag | integer | 1 when this month is strictly warmer than every previous same-calendar-month in the region's history (0 otherwise). (unit: flag) |
| row_hash | string | Deterministic 16-hex sha256 of region + date + anomaly (idempotency key). (unit: string) |
First 10 sample rows — a preview, not the complete dataset.
| date | year_month | region | region_label | anomaly_c | rolling_12m_mean_c | record_warm_month_flag | row_hash |
|---|---|---|---|---|---|---|---|
| 1880-01-01 | 1880-01 | GLB | Global | -0.19 | — | 1 | 7041b017e7400207 |
| 1880-02-01 | 1880-02 | GLB | Global | -0.26 | — | 1 | b0373909fe61bd97 |
| 1880-03-01 | 1880-03 | GLB | Global | -0.1 | — | 1 | 9be7957088c3af66 |
| 1880-04-01 | 1880-04 | GLB | Global | -0.17 | — | 1 | 5ba540c78a3d075b |
| 1880-05-01 | 1880-05 | GLB | Global | -0.11 | — | 1 | 0ef76cae1cd04ff3 |
| 1880-06-01 | 1880-06 | GLB | Global | -0.22 | — | 1 | f5a063df54255250 |
| 1880-07-01 | 1880-07 | GLB | Global | -0.19 | — | 1 | 9946bc55c31301b0 |
| 1880-08-01 | 1880-08 | GLB | Global | -0.11 | — | 1 | 7e8bf4f6c7f49a5e |
| 1880-09-01 | 1880-09 | GLB | Global | -0.15 | — | 1 | 3dafeb5a9214c6a2 |
| 1880-10-01 | 1880-10 | GLB | Global | -0.24 | — | 1 | c1bfdc80ddda8600 |
- Current
20261001T102050Z-97da23648daf · sha256 97da23648daf…
5,280 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/nasa_gistemp_intel/global_temperature_anomaly_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/nasa_gistemp_intel/global_temperature_anomaly_monthly").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/nasa_gistemp_intel/global_temperature_anomaly_monthly
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20261001T102050Z-97da23648daf and its content hash, so readers get exactly the data you used.
NASA GISTEMP Temperature Anomaly (keyless). (2026). Global temperature anomaly, monthly (GISTEMP) [Data set, snapshot 20261001T102050Z-97da23648daf, sha256 97da23648daf]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/en/datasets/nasa_gistemp_intel/global_temperature_anomaly_monthly?snapshot=20261001T102050Z-97da23648daf
@misc{dz_nasa_gistemp_intel_global_temperature_an_97da2364,
title = {{Global temperature anomaly, monthly (GISTEMP)}},
author = {{NASA GISTEMP Temperature Anomaly (keyless)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/nasa_gistemp_intel/global_temperature_anomaly_monthly?snapshot=20261001T102050Z-97da23648daf}},
note = {Snapshot 20261001T102050Z-97da23648daf, sha256 97da23648daf500777030bcb8161ee8e97ae988a84c31b228cfdbf43c29ebe51; accessed 2026-10-01}
}Embed a table or a chart
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<iframe src="https://datazimuts.com/embed/chart?dataset=nasa_gistemp_intel%2Fglobal_temperature_anomaly_monthly&lang=en&theme=auto&snapshot=20261001T102050Z-97da23648daf&x=date&y=anomaly_c&agg=avg" title="Global temperature anomaly, monthly (GISTEMP)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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