How Climate Twins finds a place's future climate today

The question

A climate analog is a location whose present climate resembles the projected future climate of another location. Analogs translate abstract projections, such as “3 °C warmer summers”, into a place people can picture, and they have a long history in ecology and adaptation planning, from the study of novel and disappearing climates (Williams et al. 2007; Williams & Jackson 2007) to maps of analogs for North American cities (Fitzpatrick & Dunn 2019).

For each place, scenario and period, the map reports the grid cell whose 1991–2020 climate is closest to the place's projected climate, four further good matches, the land area within 2σ, and how strongly the individual climate models agree. When no cell is close, the map says so rather than drawing a misleading arrow.

Places

North America (789 places): 634 in the contiguous United States, 23 in Alaska, 56 in Canada and 76 in Mexico, drawn from the GeoNames gazetteer. Places were selected greedily by population with a minimum spacing that widens as population falls (in the United States: 100,000 people at 25 km spacing, then 25,000 at 45 km, 8,000 at 75 km and 1,000 at 120 km; in Canada and Mexico: 250,000 at 35 km, 40,000 at 110 km and 5,000 at 220 km), so that large cities are all present and rural regions still have coverage. Two North Carolina places were added by hand.

World (823 cities): every city of at least one million people outside the United States, Canada and Mexico; all 202 national capitals (Natural Earth); Honolulu, which lies outside the North American grid; and cities chosen by population with minimum spacing to cover sparsely populated regions. Together they span 192 countries and territories. Country names come from Natural Earth, shortened to common forms.

Present-day climate

Climate is described by sixteen numbers: the mean daily maximum temperature, mean daily minimum temperature, mean dewpoint and total precipitation for each meteorological season (December–February, March–May, June–August, September–November). Precipitation enters the comparison as log(mm + 1), so that a given relative change counts similarly in wet and dry climates.

North America

Present climate comes from the AdaptWest 1991–2020 monthly normals at 1 km, generated with ClimateNA v7.30 (AdaptWest Project 2022; Wang et al. 2016). For the comparison grid, 1 km cells were averaged into 15 km blocks on a Lambert azimuthal equal-area projection centred at 45° N, 100° W, keeping blocks that are at least 25% land: 97,488 cells. Each place's own present climate is the mean of the 1 km cells within 10 km of it.

Worldwide

Present climate comes from TerraClimate (Abatzoglou et al. 2018), 1991–2020 monthly values at 1/24° (about 4 km). For the global comparison grid, values were averaged to 0.5° cells that are at least 25% land, covering all land except Antarctica and south of 60° S: 64,958 cells, including North America. Each world city's present climate is the mean of TerraClimate land cells within 10 km. Because the city and the grid use the same dataset, a city compared with its own surroundings scores close to 0σ (median 0.00σ).

Year-to-year variability

Distances are scaled by how much each season's climate varies from year to year at the place itself, estimated from 30 seasonal values (1991–2020), each series linearly detrended so that the warming trend is not counted as variability. In the contiguous United States these come from PRISM monthly 4 km data (PRISM Climate Group; Daly et al. 2008). Elsewhere, including Alaska, Canada, Mexico and all world cities, they come from TerraClimate. Where both are available, the two agree closely (TerraClimate-to-PRISM ratio of standard deviations: 1.05 for summer maximum temperature, 0.90 for precipitation).

Humidity

Humidity is described by the dewpoint, the temperature at which the air would become saturated. It tracks the moisture people feel better than relative humidity, which swings with the time of day. Present-day dewpoints come from TerraClimate monthly vapour pressure (Abatzoglou et al. 2018) everywhere, including North America, where the 1/24° values are averaged over the 3 × 3 pixels nearest each 15 km cell; a season's dewpoint is computed from its mean vapour pressure with the Magnus formula (Alduchov & Eskridge 1996). Of the four seasonal dewpoints, December–February and June–August enter the comparison, covering the humid season in both hemispheres; the other two are shown but not matched, so humidity carries weight comparable to its importance without swamping temperature and precipitation. Fourteen measures are matched in all.

Future humidity applies each model's change in near-surface specific humidity (huss), as a ratio limited to 0.5–3, to the observed vapour pressure. One model provides no humidity output for one scenario (FGOALS-g3 under SSP1-2.6); there, vapour pressure is scaled with that model's temperature change at constant relative humidity, following the Clausius–Clapeyron relation.

Projected climate

Projected climate follows the change-factor (delta) approach (e.g. Maraun & Widmann 2018): each model's simulated change is added to the observed present climate at the place, which removes the model's own bias in the baseline.

• Periods. 2041–2060 (“2050”) and 2081–2100 (“2100”), the IPCC AR6 mid- and end-of-century periods, compared with 1991–2020. The model baseline joins each model's historical run (1991–2014) to the chosen scenario (2015–2020).
• Scenarios. SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 from ScenarioMIP (O'Neill et al. 2016; Riahi et al. 2017).
• Variables. Monthly near-surface daily maximum and minimum temperature and precipitation (CMIP6 tasmax, tasmin, pr), as monthly climatologies for each window.
• Extraction. Model values at a place are a land-weighted Gaussian average of nearby model grid cells, with a length scale of one grid spacing, cut off at two spacings, and weights multiplied by each cell's land fraction so that ocean cells do not dilute land climate.
• Applying the change. Temperature changes are added month by month. Precipitation changes are applied as ratios, limited to the range 0.2–5. Where a model's baseline month has no precipitation at all, the ratio is set to 1. The minimum temperature is kept at least 0.1 °C below the maximum.
• Ensemble mean. The map's main answer uses the mean of the models' projected climates. Precipitation is averaged in millimetres before the log transform.

Here “obs” is the observed 1991–2020 value at the place, and “fut” and “base” are the model's own future and 1991–2020 means for the same month.

Climate models

The map uses 24 CMIP6 models (Eyring et al. 2016), one simulation each, read from the Pangeo CMIP6 cloud archive (Abernathey et al. 2021). Several CMIP6 models warm faster in response to CO₂ than observations and other lines of evidence support. Following Hausfather et al. (2022), the default ensemble keeps the 15 models whose transient climate response (TCR) lies in the IPCC AR6 likely range of 1.4–2.2 °C (Forster et al. 2021, Table 7.SM.5). The remaining nine can be added under Advanced on the map: seven above the range, one below it (INM-CM4-8) and one without a TCR in that table (EC-Earth3-Veg-LR). Including them mostly shifts results toward hotter outcomes.

Measuring similarity

Two climates are compared relative to the natural year-to-year variability of the place being studied. A difference of 1 °C in summer highs matters more at a place where summers barely vary than at one where they swing widely, and a mismatch in a pattern that rarely varies matters more than one in a pattern that varies a lot.

For place t, each of the 14 matched measures is standardised by its detrended interannual standard deviation s. The correlations among the 14 detrended series, estimated from only 30 years, are stabilised with Ledoit–Wolf shrinkage toward the identity matrix (Ledoit & Wolf 2004), with the shrinkage intensity α estimated from each place's own data (median 0.44 in North America, range 0.15–0.70; median 0.40 for world cities, range 0.16–0.76). The distance between the place's projected climate x and a candidate cell's present climate y is the Mahalanobis distance in this metric:

Following Mahony et al. (2017), D is converted to sigma dissimilarity: its percentile in a χ distribution with k = 14 degrees of freedom is re-expressed as the number of standard deviations of a one-dimensional normal distribution with the same two-sided tail probability. This makes distances comparable across places and dimensions, and gives the unit a familiar meaning.

Seasons that almost never rain

In very dry seasons (for example in Lima, Cairo or Khartoum) precipitation barely varies between years, which makes the distance undefined. For world cities, any standardised series with a standard deviation below 0.10 (in log precipitation, roughly 10%; or 0.10 °C) receives a fixed, seeded, zero-mean random series that raises its standard deviation exactly to that floor without correlating with the other measures. This affected 26 cities, mostly in a single measure. No North American place needed it.

Choosing and presenting matches

• Best match: the cell with the smallest σ for the ensemble-mean projection.
• Further matches: the next best cells at least 150 km (North America) or 300 km (worldwide) from every match already chosen, up to five in total. Matches under 1σ are about equally good.
• Area within 2σ: the summed land area of cells under 2σ, outlined on the map (true cell areas on the global grid, using latitude and land fraction).
• Model agreement: the share of individual models whose own best match lies within 500 km of the ensemble's (1,000 km for worldwide searches).
• A second method: as a robustness check, distances are recomputed with principal-component truncation instead of shrinkage, dropping components that carry under 1% of the year-to-year variance (as in Mahony et al. 2017).
• Confidence: high when at least 60% of models agree and the second method's best match is within 500 km; low when under 40% agree, or when the second method disagrees and fewer than 60% agree; medium otherwise.
• No match: when the best σ is 4 or more, or 2 or more with the best cell at the place itself, the map states that nowhere in the comparison area has this climate today and draws a ring instead of an arrow.
• Worldwide matches for North American places: when the best North American match is 2σ or worse, the place is also searched against the global grid, using its TerraClimate present climate so that both sides come from the same dataset.
• Labels: each cell is named after its nearest GeoNames populated place (worldwide: of at least 1,000 people), with the distance when it is not close.

All of this is computed in the reader's browser at the moment a place is chosen, from per-place metric matrices shipped with the page, so every cell on the map carries its own σ. The browser results match the Python pipeline cell for cell.

Checks and results

• Dataset consistency. For North American places, the present climate from TerraClimate and from AdaptWest differ by a median of 0.00σ (90th percentile 0.72σ).
• Self-check. Each world city's present climate compared with the global grid has a median best distance of 0.00σ. The exceptions are 53 small islands and narrow coastal strips (for example Malé, Funafuti, Majuro, Aden, Dakar and Saipan) whose climate no 0.5° cell resembles; their arrows should be read loosely.
• Model processing. Change factors from the global model fields reproduce the regional North American extraction at 5 places × 24 models × 4 scenarios, with a 99th-percentile difference of 0.01 °C (storage rounding).
• Browser against pipeline. The page and the Python batch pick the same best cell for North American places and for all 12 world cities tested. One known near-tie (Raleigh, 0.050σ against 0.053σ for two neighbouring cells) resolves differently because of rounding.

Most tropical lowland cities have no match by 2100 under higher emissions, because their projected heat has no present-day counterpart anywhere, consistent with earlier global analyses of novel climates (Williams et al. 2007).

Climate type, hardiness zone and growing season

These are computed from monthly normals by the same rules for a place today, the same place in the future (the ensemble-mean monthly climate) and every cell of both comparison grids, so the three columns in the comparison are always like with like.

Climate type follows the Köppen–Geiger criteria of Beck et al. (2018), which use a 0 °C boundary between temperate and cold climates (Peel et al. 2007), with summer taken as the warmer half of the year.

Hardiness zone is estimated, and is not the official USDA Plant Hardiness Zone Map. The quantity behind the zones, the average of each year's coldest temperature, is estimated from the coldest month's mean low, the annual temperature range and annual precipitation by a regression fitted to the official 1991–2020 grid from the PRISM Climate Group and USDA Agricultural Research Service (2023) across the contiguous United States (R² = 0.96; median error 1.0 °C). The estimate falls in the same half-zone as the official grid for 56% of 15 km cells and within one half-zone for 95%. Outside the contiguous United States the same formula is applied without local calibration.

Growing season is the estimated number of days between the last spring frost and the first fall frost. It sums, over the months, the days in each month times a normal probability that the month is frost-free given its mean low, fitted to ClimateNA's frost-free period over North America (Wang et al. 2016; R² = 0.96; median error 7 days).

Snowfall is not estimated. Monthly averages cannot separate rain from snow reliably: in months whose average temperature is above freezing, cold spells still bring snow. Even ClimateNA's precipitation-as-snow puts Denver's snow near 3% of its precipitation, far below what falls there.

Already happening

For each place, the average of the most recent ten complete years in TerraClimate (2016–2025) is compared with its 1991–2020 average from the same dataset, so the difference reflects climate rather than differences between datasets. The page reports the change in annual mean temperature and, where the projected change is large enough to compare with, the share of the warming projected by around 2050 under SSP2-4.5 that has already occurred. Ten years is short, so part of any difference is natural variability.

Sea level

For places within 50 km of a projection site, the page shows relative sea-level rise from the IPCC Sixth Assessment Report (Fox-Kemper et al. 2021), using the medium-confidence projections at tide gauges and on a 1° coastal grid (Garner et al. 2021). Values are the median and the likely range (17th–83rd percentiles) for 2050 and 2100 relative to 1995–2014, and include vertical land motion such as subsidence. 656 places have projections. Low-likelihood, high-impact outcomes involving rapid ice-sheet loss are not included and could add substantially by 2100.

Updates and versions

The data are rebuilt from the original sources each year by a public, automated pipeline (source code in the project repository). Each rebuild is checked before release: counts of places, grid cells and models; a self-check that today's climate at each place finds itself (current median 0.00σ); and a comparison with the previous release, flagging for review if more than 10% of best matches move over 500 km or the median change in σ exceeds 0.25. A browser test loads the page with the new data. Nothing is published until the rebuild is reviewed. A monthly check watches for a new TerraClimate year, corrected or withdrawn CMIP6 datasets and the arrival of CMIP7 projections. This page describes method version 10.0, data version 2026-09-26, using 24 models.

Why many tropical places have no match

Places near the equator are far more likely to have no present-day match. Around 2100 under SSP2-4.5 (models in the likely range):

This is mainly physics, not a gap in the data or an effect of wealth: rich tropical and desert cities such as Singapore, Dubai and Darwin have no match either. Two things combine.

• There is nowhere warmer to point to. A mid-latitude city's future climate usually exists today somewhere closer to the equator. The hottest places on Earth have no hotter place to match, so their future climates are new to the planet. Williams et al. (2007) found such novel climates concentrated in the tropics and subtropics.
• Tropical climates vary little from year to year. σ measures change against a place's own normal swings, so a given warming stands out far more where years are steady. This is why tropical climates are expected to leave their historical range first (Mora et al. 2013). It also means the change is large relative to what local ecosystems and people are adapted to.

Data play a smaller part. Where weather stations are sparse, TerraClimate can understate year-to-year variability, which inflates σ; the variability floor (above) limits this, but some tropical results are more uncertain than mid-latitude ones.

The pattern also has a moral dimension. The places facing climates with no present-day equivalent are largely those that have contributed least to the emissions causing the change (King & Harrington 2018).

Limitations

• No extremes. Heat waves, heat index, cold snaps, hurricanes, wildfire and snowfall are not included. Two places can match on these fourteen measures and still differ in ways that matter to people.
• Estimated features. Hardiness zones and growing seasons are statistical estimates from monthly data, calibrated in North America only.
• Coarse model changes. Changes come from model grid cells roughly 50–300 km across and are added to local climate, so changes in fine-scale patterns, along mountains and coasts for example, are not captured.
• Grid resolution. The comparison grids (15 km and 0.5°) smooth mountain and coastal climates. A mountain or coastal town's best match can be somewhat worse than it would be at finer resolution.
• One simulation per model. Natural variability within each model is not sampled, so part of the spread between models is internal variability rather than model difference.
• Present-day pool. Matches are found among 1991–2020 climates, which already include some warming. Present climates elsewhere will also change, which the map does not show.
• Mixed variability sources. Places outside the contiguous United States use TerraClimate for year-to-year variability, which is coarser than PRISM.
• Scenarios are not forecasts. SSP5-8.5 in particular is now widely considered implausible for this century. Each arrow is a way to picture a scenario, not a prediction for a specific town.

Software

Data processing uses Python with NumPy, SciPy, pandas, xarray (reading the CMIP6 Zarr stores), rasterio, pyproj and Shapely. The interactive map uses MapLibre GL JS 5.24 with OpenFreeMap vector tiles; the heat layer, distances and arrows are computed in the browser. The North American grid uses an ellipsoidal Lambert azimuthal equal-area projection implemented in JavaScript and checked against pyproj to better than 10⁻⁷ m.

Data and attribution

Acknowledgements. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. Dataset citations for individual CMIP6 models are available from the CMIP6 Citation Service. We also thank the Pangeo project and Google Cloud Public Datasets for the analysis-ready CMIP6 archive, the AdaptWest Project, the Climatology Lab at the University of California, Merced, and the PRISM Climate Group at Oregon State University.

References

1. Alduchov, O. A., & Eskridge, R. E. (1996). Improved Magnus form approximation of saturation vapor pressure. Journal of Applied Meteorology, 35(4), 601–609. https://doi.org/10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2
2. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5, 170191. https://doi.org/10.1038/sdata.2017.191
3. Abernathey, R. P., Augspurger, T., Banihirwe, A., Blackmon-Luca, C. C., Crone, T. J., Gentemann, C. L., Hamman, J. J., Henderson, N., Lepore, C., McCaie, T. A., Robinson, N. H., & Signell, R. P. (2021). Cloud-native repositories for big scientific data. Computing in Science & Engineering, 23(2), 26–35. https://doi.org/10.1109/MCSE.2021.3059437
4. AdaptWest Project. (2022). Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.30 software (T. Wang et al., 2022). adaptwest.databasin.org
5. Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5, 180214. https://doi.org/10.1038/sdata.2018.214
6. Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28(15), 2031–2064. https://doi.org/10.1002/joc.1688
7. Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
8. Fitzpatrick, M. C., & Dunn, R. R. (2019). Contemporary climatic analogs for 540 North American urban areas in the late 21st century. Nature Communications, 10, 614. https://doi.org/10.1038/s41467-019-08540-3
9. Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., & Zhang, H. (2021). The Earth's energy budget, climate feedbacks, and climate sensitivity. In V. Masson-Delmotte et al. (Eds.), Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 923–1054). Cambridge University Press. https://doi.org/10.1017/9781009157896.009
10. Fox-Kemper, B., Hewitt, H. T., Xiao, C., et al. (2021). Ocean, cryosphere and sea level change. In V. Masson-Delmotte et al. (Eds.), Climate Change 2021: The Physical Science Basis (pp. 1211–1362). Cambridge University Press. https://doi.org/10.1017/9781009157896.011
11. Garner, G. G., Hermans, T., Kopp, R. E., Slangen, A. B. A., Edwards, T. L., Levermann, A., et al. (2021). IPCC AR6 sea level projections (Version 20210809) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5914709
12. Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W., & Zelinka, M. (2022). Climate simulations: recognize the ‘hot model’ problem. Nature, 605, 26–29. https://doi.org/10.1038/d41586-022-01192-2
13. Ledoit, O., & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis, 88(2), 365–411. https://doi.org/10.1016/S0047-259X(03)00096-4
14. King, A. D., & Harrington, L. J. (2018). The inequality of climate change from 1.5 to 2°C of global warming. Geophysical Research Letters, 45(10), 5030–5033. https://doi.org/10.1029/2018GL078430
15. Mahony, C. R., Cannon, A. J., Wang, T., & Aitken, S. N. (2017). A closer look at novel climates: new methods and insights at continental to landscape scales. Global Change Biology, 23(9), 3934–3955. https://doi.org/10.1111/gcb.13645
16. Maraun, D., & Widmann, M. (2018). Statistical Downscaling and Bias Correction for Climate Research. Cambridge University Press. https://doi.org/10.1017/9781107588783
17. Mora, C., Frazier, A. G., Longman, R. J., Dacks, R. S., Walton, M. M., Tong, E. J., et al. (2013). The projected timing of climate departure from recent variability. Nature, 502(7470), 183–187. https://doi.org/10.1038/nature12540
18. O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., & Sanderson, B. M. (2016). The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9), 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016
19. Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences, 11(5), 1633–1644. https://doi.org/10.5194/hess-11-1633-2007
20. PRISM Climate Group & USDA Agricultural Research Service. (2023). 2023 USDA Plant Hardiness Zone Map GIS data, 1991–2020 mean annual extreme minimum temperature. Oregon State University. https://prism.oregonstate.edu/phzm
21. PRISM Climate Group. (2026). PRISM monthly time series, 4 km, 1991–2020. Oregon State University. https://prism.oregonstate.edu
22. Riahi, K., van Vuuren, D. P., Kriegler, E., et al. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
23. Wang, T., Hamann, A., Spittlehouse, D., & Carroll, C. (2016). Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE, 11(6), e0156720. https://doi.org/10.1371/journal.pone.0156720
24. Williams, J. W., & Jackson, S. T. (2007). Novel climates, no-analog communities, and ecological surprises. Frontiers in Ecology and the Environment, 5(9), 475–482. https://doi.org/10.1890/070037
25. Williams, J. W., Jackson, S. T., & Kutzbach, J. E. (2007). Projected distributions of novel and disappearing climates by 2100 AD. Proceedings of the National Academy of Sciences, 104(14), 5738–5742. https://doi.org/10.1073/pnas.0606292104