The product at the heart of the release is known as CaPA-24h, an offline daily precipitation reanalysis built on the Canadian Precipitation Analysis system. It works by combining a background precipitation field generated by the reanalysis’s dynamical component with a dense network of daily surface observations through an optimal interpolation algorithm. The background field comes from a regional numerical weather prediction model coupled with a land-surface data assimilation system, while the analysis step nudges that model output toward reality wherever gauges have actually measured what fell from the sky. The result is a gridded, physically consistent precipitation record at roughly 10-kilometre resolution that spans the entire North American domain for 45 years.

Version 3.2 brings substantial technical modernization. The underlying atmospheric model, the Global Environmental Multiscale system, now runs with updated physical parameterizations, higher vertical resolution, and sharper topographic representation, and it is initialized with atmospheric conditions from the ERA5 global reanalysis rather than the older ERA-Interim. The team also fully reprocessed the Integrated Surface Database used for observations before the year 2000, incorporating trace precipitation information and more accurate station locations. These changes matter most in data-sparse regions, where the background field dominates the final analysis; there, the new version delivers systematically less biased precipitation than version 2.1.

The number of assimilated stations tells a striking story. Before 2000, the analysis ingested roughly 2,000 stations per day on average; afterward, with observations drawn from operational archives and the newly included adjusted Canadian hourly dataset, that figure jumped to about 9,000. All observations pass through automated quality control, including spatial consistency checks that compare each station against its neighbours, seasonal filters for wintertime wind effects on solid precipitation, and additional diagnostics that caught stations reporting implausibly low totals or suspicious extreme events that escaped the standard screening. Bias-corrected Canadian datasets are exempted from some filters but still undergo spatial consistency checks, and duplicate records are removed when adjusted and standard stations sit nearly on top of each other.

To evaluate the product honestly, the researchers used a leave-one-out framework: whenever a station was used to verify the analysis, it was excluded from the assimilation, so the verification reflects genuine predictive skill rather than circular self-agreement. Verification relied on the Frequency Bias Index, which measures whether events are over- or under-forecast; the Equitable Threat Score, which measures detection skill after accounting for chance; and the partial mean, which isolates conditional precipitation intensity. At station locations, the new and old versions perform comparably, which the authors attribute to the strong constraint imposed by the gauges themselves. The real gains appear in the background fields, which show reduced frequency bias and improved intensity characteristics across most regions and seasons.

Comparisons with independent gridded datasets sharpen the picture. Against ERA5-Land, the land component of a leading European global reanalysis, CaPA-24h version 3.2 provides more accurate seasonal and regional precipitation patterns. ERA5-Land tends to overestimate the frequency of light to moderate precipitation while underestimating high-intensity events, and it produces smoother fields that miss fine-scale spatial variability. Against PRISM, a high-resolution observation-based dataset for the contiguous United States, the Canadian product shows closer agreement over much of the eastern domain, reproducing sharper gradients and regional contrasts. A frequency-intensity decomposition revealed that biases in seasonal totals are usually driven by errors in wet-day frequency rather than intensity, and it exposed cases where ERA5-Land’s compensating frequency and intensity errors mask real deficiencies behind seemingly accurate totals.

Extreme precipitation tells a more nuanced story. Using the Kling-Gupta Efficiency score to evaluate indices such as the annual maximum one-day precipitation and the contribution of very wet days to annual totals, the study finds a pronounced east-west contrast. East of the major mountain ranges, the new reanalysis generally outperforms ERA5-Land in all seasons, with scores between 0.7 and 0.9 driven by near-perfect bias and correlation. In the western cordillera, however, performance degrades for both products, with orographic precipitation remaining a stubborn challenge. Both reanalyses also systematically underestimate summer accumulations over large portions of the United States relative to PRISM, a signal the authors attribute to the difficulty of capturing sub-grid convective storms rather than to observational coverage.

The team also assessed, for the first time, the hourly precipitation product derived by temporally disaggregating the daily analysis. The disaggregation preserves realistic timing from the model forecasts while enforcing consistency with the daily totals, and it successfully reproduces the expected late-afternoon peak of warm-season convective precipitation across many regions. But artificial peaks appear at fixed synoptic hours, reaching about 0.02 millimetres per hour in summer, reflecting the stitching together of forecast segments of different lead times and the model’s spin-up behaviour. These artefacts can distort event-duration statistics and derived hydrological indicators, and the authors flag more advanced, potentially machine-learning-based disaggregation methods as a priority for future versions.

For users who need data beyond December 2024, the study examines whether the operational version of the Canadian Precipitation Analysis can seamlessly extend the record. Comparing the two systems over the 2021-2022 water year showed broad consistency at climatological scales, with overlapping seasonal time series and coherent interannual variability. The operational system assimilates radar and satellite precipitation estimates during liquid-precipitation events, which improves spatial coherence but introduces localized differences, most notably in summer over eastern Canada. The authors conclude that appending the operational product is appropriate for trend assessments and seasonal-to-interannual studies, but caution is warranted for weather-scale or event-based analyses, particularly during convective regimes.

The dataset arrives at a moment when accurate precipitation records underpin everything from flood forecasting and drought monitoring to water resource management and the training of artificial intelligence weather models. Its limitations are candidly documented: biases persist in southern and western mountainous areas, the pre-2000 period is more sensitive to sparse station density, and the observing-system transition around 2000 warrants caution in long-term trend analyses. Even so, with roughly 40 surface variables distributed alongside the precipitation fields, a confidence index telling users how strongly each grid cell is constrained by observations, and public availability through the Environment and Climate Change Canada data portal, the new release offers hydrologists, climatologists, and impact researchers one of the most observationally grounded pictures yet of how water has fallen across North America over the past four and a half decades.

Subject of Research: A 45-year high-resolution precipitation reanalysis for North America combining weather model forecasts with ground-station observations

Article Title: The Canadian Surface Reanalysis (CaSR) v3.2 precipitation dataset: a 45-year high-resolution analysis for North America (1980–2024)

Article References: Khedhaouiria, D., Gasset, N., Fortin, V., Dimitrijevic, M., Bulat, M., & Wang, X. (2026). The Canadian Surface Reanalysis (CaSR) v3.2 precipitation dataset: a 45-year high-resolution analysis for North America (1980–2024). Hydrology and Earth System Sciences, 30(18), 5971-5998. https://doi.org/10.5194/hess-30-5971-2026

Image Credits: AI Generated

DOI: 10.5194/hess-30-5971-2026

Keywords: precipitation reanalysis, CaSR, CaPA, North America, data assimilation, hydrology, climate dataset, ERA5-Land, PRISM, extreme precipitation, Environment and Climate Change Canada, water resources

The reason such an effort is needed lies in the nature of the observing network itself. Surface meteorological stations are the workhorses of climate-change detection, attribution, and impact studies, and they also serve as the yardsticks against which satellite remote-sensing retrievals and climate model simulations are evaluated. But these stations are not pristine instruments floating above history. Observing instruments are continuously upgraded and replaced, and different instruments carry different biases. When a thermometer design changes or a humidity sensor is swapped for a newer model, the recorded values can shift for reasons that have nothing to do with the atmosphere. These artificial changes, known as inhomogeneities, contaminate the very records scientists rely on to detect long-term climate signals.

In principle, observational data are expected to represent large-scale climate-change signals on the order of 100 kilometers or more. Yet changes in the local environment surrounding a station, most notably urbanization, can be superimposed on those large-scale signals. To accurately detect and attribute climate change, the effects of non-climatic factors and local environmental changes must be removed as much as possible. Some of these effects are comparatively easy to handle. Abrupt changes caused by station relocations and instrument replacements tend to be large and can usually be detected and corrected through conventional homogenization techniques. The harder problem is the slow one: changes in the observing environment around a station are often gradual, and each individual change has only a small effect on the observations, making it extremely difficult to identify and correct. Over decades, however, these small effects accumulate into systematic biases that can significantly distort estimates of climate-change trends and even undermine the reliability of detection and attribution studies.

Traditional homogenization methods generally rely on comparisons with neighboring stations. A set of reference stations with similar elevation and reliable data quality is selected around a target station, and possible inhomogeneities are identified by comparing their time series. The logic is sound when the neighbors are clean. But when many stations in a region are simultaneously affected by instrument sensitivity drift, instrument replacement, urbanization, and other factors, the reference stations themselves may contain inhomogeneities. In that case, the method can miss the very signals it is designed to find, and its ability to detect both gradual and abrupt inhomogeneities becomes limited. This is precisely the situation across much of China, where the national station network has undergone sweeping technological and environmental change over the past sixty years.

To break this deadlock, Wang’s team spent more than a decade developing a homogenization method based on same-station comparison. Instead of leaning on neighboring stations that may share the same hidden biases, the approach overcomes key difficulties in detecting and correcting gradual inhomogeneities, and it enables the detection and correction of both gradual and abrupt inhomogeneities in China’s land-surface climate observations. The variables it covers are the essential ones for climate monitoring: surface solar radiation, wind speed, relative humidity, air temperature, ground temperature, and precipitation. The method provides the technical backbone for building high-quality climate datasets for China, and HCD01 is the first major product of that backbone.

The corrections the team uncovered are striking, and they change how several well-known features of China’s climate record should be interpreted. Before 1990, strict calibration instruments and procedures were lacking, and gradual inhomogeneity caused by instrument sensitivity drift crept into China’s surface solar radiation observations. The consequence is significant: the widely reported decreasing trend in surface solar radiation during 1960 to 1990 was seriously overestimated. Then, during 1990 to 1993, instrument updates caused a sudden increase in the observations, an artificial step that had nothing to do with the sun or the clouds. In other words, part of the famous global dimming and brightening story as recorded over China was an artifact of the instruments rather than the atmosphere.

The twenty-first century brought a different kind of discontinuity. China’s meteorological observations began shifting from manual to automatic observation, and the greatest impact fell on relative humidity. Under conditions of low near-surface wind speed, the dry- and wet-bulb thermometers used in manual observations tended to overestimate relative humidity, whereas the capacitive sensors used in automatic observations did not have this problem. The transition between the two methods therefore produced a false decreasing trend in relative humidity observations in the twenty-first century, a trend that reflects changing hardware rather than a drying atmosphere. Correcting this bias matters enormously, because relative humidity feeds directly into calculations of evaporation, heat stress, and the behavior of the hydrological cycle.

Urbanization left its own fingerprints on the record. The growth of cities around meteorological stations amplified the observed warming trend of daily minimum temperature, since urban surfaces store and release heat differently than the rural landscapes that once surrounded the stations. Urbanization also increased surface roughness, which reduced near-surface wind speed as measured at those sites. Meanwhile, relocations of meteorological stations from urban to rural areas caused abrupt increases in observed wind speed, the opposite artifact. The team notes that although the decline in station-observed near-surface wind speed does not represent large-scale change, it does reflect real changes in near-surface wind speed at the observing sites themselves, and it affects station precipitation observations, since wind influences how effectively rain gauges catch falling precipitation.

Cleaning the station records, however, is only half the battle. Homogenized station observations alone remain insufficient for climate-change research because observing stations are sparse and unevenly distributed across China, and because observation periods differ among stations. That makes direct spatiotemporal analysis at the national scale difficult. To solve this, the team merged the homogenized station observations with ERA5-Land reanalysis data to construct HCD01, providing complete spatial coverage at a daily, 0.1-degree resolution for 1961 through 2022. The results show that HCD01 clearly improves the representation of long-term trends compared with both reanalysis data alone and raw observations, offering a high-quality data basis for studies of regional climate change and its impacts across China.

The study was led by Professor Kaicun Wang, who served as first author and corresponding author. The research team included current graduate students Hongze Cai, Yun Li, Hanmeng Xia, and Changjian Yin from Wang’s group, along with former graduate students including Professor Chunlue Zhou and Associate Professor Yanyi He, both of Sun Yat-sen University, Young Researcher Zhengtai Zhang of Lanzhou University, and Dr. Runze Zhao of the National Satellite Meteorological Center of the China Meteorological Administration. The work was supported by the National Key Research and Development Program of China and the Science and Technology Program of Guizhou Province. With HCD01 now openly available, researchers studying everything from agricultural water demand to solar energy potential in China have access to a climate record in which the instruments, the cities, and the station moves have been carefully accounted for, leaving the climate signal standing on its own.

Subject of Research: Homogenization of Chinese meteorological station records and construction of a gridded climate dataset

Article Title: Peking University team releases homogenized gridded climate dataset for China

Article References: Peking University team releases homogenized gridded climate dataset for China. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: climate dataset, homogenization, China, meteorological stations, solar radiation, relative humidity, urbanization, ERA5-Land, reanalysis, climate change detection, Peking University, Science China Earth Sciences