Millions of rescued handwritten records reveal Congo Basin warming

Deep inside the archives of the Institut National pour l’Etude et la Recherche Agronomiques in Yangambi, in the Democratic Republic of the Congo, thousands of bundles of yellowed paper have been quietly recording the climate of Central Africa for more than a century. Each bundle holds handwritten monthly observation sheets from weather stations scattered across the country, filled with daily maximum, minimum and average temperatures, rainfall totals, and dry and wet bulb readings taken three times a day. For decades these records were effectively invisible to climate science, locked away on fragile paper in a region that the Intergovernmental Panel on Climate Change has never been able to assess for changes in hot extremes and extreme precipitation since the 1950s. A new study published in Earth System Dynamics has now changed that, transforming nearly ten thousand of these sheets into more than one million machine-readable daily weather observations and using them to document a clear, accelerating warming signal across the Congo Basin.

The scale of the rescue effort is remarkable. During two field campaigns in 2023, a team led by Derrick Muheki of the Vrije Universiteit Brussel first spent two weeks compiling a comprehensive inventory of the archives, cataloguing station names, codes, coordinates and altitudes, and verifying that each bundle of sheets was correctly assigned to its station, year and month. They then spent two months photographing every available sheet using a digital camera mounted on a tripod inside a portable photo box with uniform lighting. Because the archive site has no electricity, the entire setup ran on rechargeable batteries. In total, 9885 sheets from 37 weather stations across the Democratic Republic of the Congo were imaged, covering records that mostly span from the 1960s to the mid-1990s, with a few extending into the early 2000s. The urgency was real: a fire had damaged one wing of the Yangambi archive just months before the digitization campaign began.

Photographing the sheets was only the first step. Reading them posed a far greater challenge, since every value was handwritten, in styles that varied from observer to observer and decade to decade, on paper of wildly varying quality and condition. To solve this, the team applied an improved version of MeteoSaver, an open-source software package they developed that uses machine learning to transcribe handwritten tabular data. The software processes each image through a pipeline of modules: it enhances the image with adaptive threshold binarization, detects the table structure and individual cells using computer vision algorithms, recognizes the handwritten values with a trained Tesseract optical character recognition model, and then subjects every transcribed value to a battery of automated quality control checks before formatting the results in the standardized Station Exchange Format prescribed by the Copernicus Data Rescue Service.

The quality control system is where the technical ingenuity really shows. Each transcribed value is checked against region-specific temperature thresholds, logical consistency rules and inter-variable relationships. Because the original observers calculated five-day and monthly totals and averages on every sheet, the software can recalculate these summaries from the transcribed daily values and compare them with the handwritten ones, confirming entire blocks of data when they agree within a 0.2 degree Celsius margin that accounts for historical rounding. The sheets also record dry bulb temperature, wet bulb temperature, actual vapour pressure, relative humidity and vapour pressure deficit at 06:00, 15:00 and 18:00 local time, and these variables are physically linked by well-known atmospheric equations. The software exploits those relationships, using a modified Magnus-Tetens formula and the psychrometric equation from the Smithsonian meteorological tables, to cross-validate the transcribed values and flag or correct inconsistencies. This iterative approach paid off dramatically: after five consecutive runs over the archive, in which newly confirmed transcriptions were fed back into the training data, the number of quality-controlled confirmed values rose from about 321,000 to more than 1,019,000, a 215 percent increase.

Validation against independently hand-transcribed records from five stations showed that the automated pipeline achieved match rates between 81.2 and 92.3 percent, with a median of 87.4 percent, and a median mean absolute error of 0.35 degrees Celsius. That error is of the same order as the observational uncertainty of historical thermometer measurements in the tropics, and part of the disagreement likely stems from errors in the manual reference data itself rather than the machine. Most mismatches were not misread digits but one-day shifts caused by row misalignment in table detection, a problem the authors identify as a priority for future improvement. Combined with previously transcribed datasets, including the COBECORE project’s pre-1960 records from the Belgian State Archives and a manually transcribed series for Yangambi-Km.5 extending to 2020, the new dataset spans 1908 to 2023 and forms the largest in situ temperature and precipitation dataset ever assembled for the Congo Basin.

What the rescued records reveal is unambiguous. Of the 21 stations with sufficient data for trend analysis, 20 show warming in at least one of daily maximum, minimum or average temperature. The median trends are 0.22, 0.10 and 0.15 degrees Celsius per decade respectively, corresponding to roughly 0.7, 0.3 and 0.5 degrees Celsius of warming over a 30-year period. These magnitudes are consistent with previous regional assessments that estimated 0.75 to 1.05 degrees Celsius of warming in Central Africa since 1960, but they now rest on direct, station-level evidence from within the basin itself. The analysis, based on the Theil-Sen estimator, a robust method for detecting trends in noisy time series, also shows that the temperature distributions themselves are shifting: with each successive decade between 1961 and 1990, the probability distributions of daily maximum, minimum and average temperatures move progressively toward warmer values, with modal temperatures and 95th percentile extremes rising by nearly a degree Celsius between the first and last decades at several stations.

The changing face of temperature extremes is perhaps the most striking finding. Across the aggregated station network, the average number of hot days per year, defined as days exceeding the 95th percentile of daily maximum temperature, nearly doubled between the earliest and most recent decades of the common observation window, rising from about three days per year above 33.3 degrees Celsius to about five. Days with average temperatures above 26.9 degrees Celsius doubled from roughly three to six per year. Meanwhile, cold days, those falling below the 5th percentile, declined from around seven to five per year, with cold nights showing a similar decline. At individual stations with longer records, such as Luki-Plateau, Mulungu-Mulohe and Yangambi-Km.5, the number of hot days more than tripled over the period 1961 to 2000. Because the IPCC’s Sixth Assessment Report could not assess changes in hot extremes in Central Africa at all, these observations fill a genuine hole in the global picture of climate change, providing the raw material that future detection and attribution studies will need.

The precipitation story is more tentative. Only three stations had more than 30 years of quality-controlled daily rainfall data, and at two of them, Yangambi-Km.5 and Mulungu-Mulohe, heavy precipitation events became markedly more frequent in the later half of the record. At Mulungu-Mulohe, a daily rainfall of 63 millimetres, which had a return period of about two years in the earlier period, became roughly a once-a-year event in the later period. At Yangambi-Km.5, an 83-millimetre day likewise became twice as frequent. The third station, Gimbi-Plateau, showed no substantial change. With such limited spatial coverage, the authors are careful to present these as station-specific examples rather than a representative picture of rainfall change across the Democratic Republic of the Congo, a caution that reflects the harder quality control problem posed by precipitation, for which fewer automated consistency checks are available.

The team also compared their station trends with the ERA5-Land reanalysis, a state-of-the-art gridded product from the European Centre for Medium-Range Weather Forecasts. The reanalysis captures the seasonal cycle and the broad warming trend from the 1960s onward reasonably well, but shows no systematic pattern of agreement or disagreement with the stations, sometimes underestimating and sometimes overestimating local trends. More troubling, ERA5-Land exhibits a pronounced artificial jump of about 4.5 to 5 degrees Celsius in its temperature series during the early 1950s that appears in none of the station records from that era, a sign that the reanalysis is weakly constrained in this region during its early period when few observations were available to anchor it. The rescued dataset, now uploaded to open-access repositories including the Copernicus Global Land and Marine Observations Database and Zenodo, offers exactly the kind of ground truth needed to fix such products.

The implications reach well beyond Central Africa. Most station records end in the mid-1990s, when the First and Second Congo Wars disrupted observations nationwide, meaning the full acceleration of warming since the 1990s is likely not captured in these data. Roughly 300,000 additional temperature records and 200,000 precipitation records were transcribed but did not yet pass quality control, and 225 hand-drawn sheets were excluded entirely, all of which could be recovered with improved software or citizen science verification. Meanwhile, the COBECORE project has imaged records from 575 pre-1960 stations that could add millions more observations if transcribed. The authors present their workflow, from inventory to imaging to machine-learning transcription to quality control, as a replicable template for other data-sparse regions where millions of weather observations still moulder on paper. For a basin that holds the world’s second-largest river and rainforest, and some of its most climate-vulnerable populations, turning those fragile pages into hard numbers is not just an archival triumph; it is the foundation for understanding, and preparing for, the region’s warming future.

Subject of Research: Climate data rescue and observed warming trends in the Congo Basin

Article Title: From paper to proof: revealing Congo Basin warming through rescued climate archives

Article References: Muheki, D., Hufkens, K., Jacobsen, K., Verbeeck, H., Boeckx, P., Kankonde Ntumba, D., Kapalay Moulasa, O., Vercruysse, B., Birkholz, J. M., Verbruggen, C., Hawkins, E., Lampe, S., Yakusu, E. K., Imwangana, F. M., Mbifo, J., Likwela, T. B., Meunier, F., Dewitte, O., Thorne, P., & Thiery, W. (2026). From paper to proof: revealing Congo Basin warming through rescued climate archives. Earth System Dynamics, 17(5), 1455-1511. https://doi.org/10.5194/esd-17-1455-2026

Image Credits: AI Generated

DOI: 10.5194/esd-17-1455-2026

Keywords: Congo Basin, climate data rescue, Democratic Republic of the Congo, temperature trends, extreme heat, machine learning, MeteoSaver, historical weather records, ERA5-Land, precipitation extremes, IPCC, Earth System Dynamics