Global rainfall maps are assembled from observations collected by multiple satellites, but the satellites cannot observe the entire Earth at once. As a result, some mapped areas may have no valid observations. Current methods attempt to fill these gaps by estimating precipitation from observations made before and after a missing period. Researchers at Chiba University said this approach may not fully capture how precipitation patterns change over time, which can lead to inconsistencies in the maps.

To address the problem, Assistant Professor Daiko Kishikawa, Yuka Muto and Shunji Kotsuki treated precipitation maps as video-like sequences. The method uses information from nearby time frames and surrounding regions, together with additional contextual inputs, to reconstruct missing sections. The team’s proposal, titled “Conditional Diffusion Models for Global Precipitation Map Inpainting,” was published in Volume 13, Issue 9 of Earth and Space Science. It is a modeling study: the authors developed and tested a computational method for filling gaps rather than conducting a field trial of an operational rainfall-monitoring system.

The model combines a three-dimensional U-Net, a convolutional neural-network architecture commonly used for image segmentation, with a 3D condition encoder and a conditional diffusion model. The U-Net captures precipitation patterns across space and time. The condition encoder processes supplementary information, including infrared satellite images, geographic coordinates and time, and supplies contextual details to the reconstruction model. The diffusion model then estimates the missing precipitation information. This arrangement allows the system to use both temporal continuity and surrounding spatial information when producing a complete map sequence from incomplete input.

Training the system required a way to reproduce the gaps found in satellite products. The researchers used hourly precipitation data from the ERA5 reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts, covering 2020 through 2023. ERA5 provides complete precipitation maps. To create incomplete maps that resembled GSMaP, the team identified the locations of missing observations in the Global Satellite Mapping of Precipitation, produced by the Japan Aerospace Exploration Agency, and removed the corresponding areas from the ERA5 maps. These “pseudo-GSMaP” maps served as model inputs, while the complete ERA5 fields provided target information during training.

The approach was then tested on 2024 data. According to the study report, the conditional diffusion model achieved high prediction accuracy and image quality compared with the three comparison approaches: a U-Net, a variational autoencoder and a generative adversarial network. Its reconstructed precipitation patterns also remained consistent across locations and over time. The study therefore evaluated both numerical performance and the spatiotemporal coherence of the generated rainfall fields, although the source material does not provide a separate field-trial assessment of the model’s operational use.

Sensitivity analysis identified precipitation data and infrared satellite images as the most important inputs for accurately reconstructing missing precipitation. The result indicates that the two input types contributed more strongly than the other encoded information to reconstruction performance. The precipitation fields provide the sequence whose missing areas are being repaired, while the infrared satellite images are among the additional data processed by the condition encoder. The authors also encoded geographic coordinates and time, but the reported sensitivity analysis found precipitation and infrared imagery to be the leading contributors.

The researchers subsequently applied the model to real GSMaP maps that contained missing regions. A complete GSMaP dataset was not available for direct comparison, so ERA5 data served as the reference. In this application, the model reconstructed the missing portions of genuine satellite observations, showing that the method could operate on real gap patterns. The two evaluations used different forms of evidence: the 2024 test provided complete target maps for assessing model performance, while the real-map application was compared with an external reanalysis reference rather than a complete version of the same satellite product.

The authors say the method could be adapted for near-real-time applications involving heavy-rainfall monitoring, water-resource management, hydrological analysis and disaster-risk reduction. They also note that more continuous rainfall information could be valuable in regions with sparse observations, including places with few ground-based rain gauges or weather radars. These are proposed applications rather than demonstrated outcomes. The reported evidence establishes the model’s reconstruction performance in computational tests and its ability to fill real GSMaP gaps; it does not show that the method has prevented floods, improved weather forecasts or produced measurable public-safety benefits.

Overall, the study presents a way to treat global precipitation maps as space-time data rather than as isolated images. Its conditional diffusion model combines a 3D U-Net with contextual encoding, learns from reanalysis maps with simulated satellite-style gaps and reconstructs missing observations in both held-out and real GSMaP inputs. Within the limits of the available evaluation, the results show higher accuracy and image quality than the tested deep-learning alternatives, with precipitation and infrared imagery identified as the most important inputs. The work offers a computational route to more continuous global rainfall maps while leaving its proposed near-real-time and resource-management uses for subsequent application.

Subject of Research: Machine-learning reconstruction of missing observations in global satellite precipitation maps

Article Title: Filling the gaps in global rainfall maps

Article References: Filling the gaps in global rainfall maps. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: global rainfall maps, precipitation reconstruction, conditional diffusion models, 3D U-Net, satellite observations, GSMaP, ERA5 reanalysis, remote sensing, spatiotemporal modeling, Filling, gaps, global

The innovation lies in what the researchers chose to work with. Widely used drought atlases, including the North American Drought Atlas, generally target a single variable: the summer Palmer Drought Severity Index, a measure of soil moisture that reflects a complex mixture of meteorological conditions. The index responds not only to rainfall but also to evapotranspiration, which depends on temperature, humidity, wind speed, and radiation, and it integrates conditions over the preceding year or more. That complexity, the team realized, is an opportunity rather than a limitation. Because the Palmer index is shaped by both temperature and precipitation, the reconstructed values published in the drought atlas can be mathematically inverted to infer the underlying meteorology, albeit with considerable uncertainty that must be handled rigorously.

To do this, the researchers built a hierarchical Bayesian model with three components. First, they estimated a forward model describing how the Palmer index responds to monthly temperature and precipitation, including a lag term capturing soil moisture memory, so that a dry year tends to be followed by another dry year. Second, they learned a prior distribution on the 24-dimensional vector of monthly temperatures and precipitation, spanning September through August, that captures the observed covariance structure between these variables. Third, they combined these elements to compute a full posterior distribution for unobserved past climate. The prior was constructed with an LKJ distribution on the covariance matrix, and the model was fitted using the PyMC probabilistic programming framework with the No-U-Turn Sampler, an adaptive variant of Hamiltonian Monte Carlo.

One unavoidable complication is that all modern weather occurs against the backdrop of rapid global warming. Because current global temperatures are unprecedented in the instrumental record, the basic assumption of most learning models, that training data resembles validation data, breaks down. The team addressed this by assuming that regional monthly temperatures and precipitation scale with global mean temperature, using the GISS Surface Temperature Analysis to estimate the anomaly. Over the reconstruction period, where no instrumental global temperature exists, they treated last-millennium global temperature as a random variable with a distribution informed by the variability of the detrended twentieth-century record. This allowed the model to separate the forced warming response from internal variability when learning what past climate might have looked like.

The three study locations were chosen deliberately, each a hotspot of past drought activity with a distinct precipitation climatology. Dodge City, Kansas, sits at the heart of the Dust Bowl, where rain falls mainly in spring and summer. Red Mesa, Arizona, near the Four Corners region, is the epicenter of historical megadrought activity, with precipitation spread more evenly through the year. Sonora Junction, California, in the Sierra Nevada, receives almost all its precipitation in winter. The fitted forward model revealed these differences clearly: in Kansas, high summer drought index values were robustly associated with cool, wet springs and summers; in Arizona, the index was more sensitive to fall and winter precipitation; and in California, winter rainfall and even small amounts of June rain strongly influenced the index, while the previous year’s conditions mattered little.

Validation against the CRU TS instrumental dataset showed the method performs well, especially for precipitation. Correlations between the posterior mean reconstruction and observations over the validation period reached 0.72 for Dodge City, 0.72 for Sonora Junction, and 0.78 for Red Mesa. The reconstruction captured the Dust Bowl drought in Kansas better than the multi-proxy Last Millennium Reanalysis, and instrumental precipitation values fell within the reconstructed likely range for most years. Temperature proved harder: correlations of 0.54, 0.31, and 0.4 across the three sites reflected the simple fact that the growing-season Palmer index is far more sensitive to rainfall than to temperature. The method still showed some skill, shifting posterior temperatures toward higher values during the scorching 1930s, even if it could not capture the full magnitude of the extreme heat.

The probabilistic framework also enables something deterministic reconstructions cannot: statements about the odds that particular years or decades were the most extreme in a thousand years. By generating 4000 realizations of the posterior and concatenating them with instrumental data, the team calculated the probability that any given year was the wettest or driest on record. In Kansas, the year 1522 was the most likely driest year of the millennium, with a 13 percent probability, while 1913, the driest year in the instrumental record, had a 23 percent chance of being the driest since at least 1000. In Arizona, there was only a 0.05 percent chance the driest year occurred after 1901, with candidates including 1748, 1685, 1729, and 1542. In California, the driest year most likely fell in 1580, 1053, 1729, or 1126.

The decade-scale results carry the most dramatic implications. There is a 71 percent probability that 1931 to 1940 was the driest decade on record in central Kansas, confirming the Dust Bowl as a hydroclimatic event essentially without precedent in the past millennium. Meanwhile, the late twentieth century stands out on the wet end of the distribution: a 15 percent chance that 1992 to 2001 was the wettest decade in Kansas, and a 31 percent chance that 1979 to 1988 was the wettest decade in the Arizona region. These findings reinforce recent work identifying an extraordinary late twentieth-century pluvial in the American southwest, a wet episode that may have masked the onset of the aridification now underway.

The study also exposes an important limitation with implications for climate risk assessment. Over the training period of 1950 to 2005, the Kansas region simply did not experience hot droughts of the kind that struck in 1934, when record-shattering heat accompanied extreme precipitation deficits. The 1956 drought brought similarly low summer rainfall, but average summer temperatures were 1.67 degrees Celsius colder than in 1934. Because tree-ring drought records from the late twentieth century are relatively insensitive to temperature, a model trained on that era will systematically fail to capture combined heat and drought extremes, precisely the compound events that pose the greatest dangers in a warming world.

The authors emphasize that their method is designed to complement, not replace, existing reconstruction techniques that assimilate multiple proxies or rely on climate model priors. Because it is fully Bayesian, the framework can be updated as new data arrive and extended to incorporate greater complexity, such as autoregressive or Gaussian process models of internal variability, or independent reconstructions of global mean temperature. For now, the study demonstrates how much hidden information can be coaxed from a single, familiar dataset. The rings of centuries-old trees, it turns out, were quietly recording not just drought, but the temperature and rainfall combinations that produced it, waiting for the right statistical tools to set them free.

Subject of Research: Bayesian reconstruction of last-millennium temperature and precipitation from tree-ring-based drought atlas data

Article Title: Joint probabilistic estimates of temperature and precipitation from tree ring-based reconstructions of the last millennium

Article References: Marvel, K., Cook, B., Weng, E., Singh, R., & Cook, E. (2026). Joint probabilistic estimates of temperature and precipitation from tree ring-based reconstructions of the last millennium. Advances in Statistical Climatology, Meteorology and Oceanography, 12(1), 43-57. https://doi.org/10.5194/ascmo-12-43-2026

Image Credits: AI Generated

DOI: 10.5194/ascmo-12-43-2026

Keywords: tree rings, paleoclimate, Bayesian inference, drought, Palmer Drought Severity Index, North American Drought Atlas, Dust Bowl, precipitation reconstruction, temperature reconstruction, last millennium, megadrought, climate variability

The instrumental weather stations that dot this remote region simply have not been operating long enough to say whether the hydroclimatic changes of recent decades are unusual or just another turn of natural variability. That is precisely the gap the new reconstruction fills. By extending the precipitation record back to the mid-eighteenth century, the team led by Junhui Guo and Feng Chen of Yunnan University, working with colleagues in Russia, the United Kingdom and Tajikistan, has provided the long-term context that short observational records cannot supply. The result is one of the most complete pictures yet of how rainfall has behaved in central Siberia over nearly three hundred years.

The technical foundation of the reconstruction rests on a well-established principle of dendrochronology: trees growing in moisture-limited environments record the water available to them in the width of their annual growth rings. In cold, high-latitude regions, however, the relationship between tree growth and climate can be complicated by permafrost, snow cover and short growing seasons. The researchers therefore began by testing which seasonal window of precipitation best explained ring-width variability in their spruce cores. The answer was an unusually long one: total precipitation from May of the previous growing year to April of the current year, a period designated P5–C4. During the calibration period from 1968 to 2018, this cumulative precipitation measure correlated with tree growth at r = 0.649, a highly significant relationship with a p-value below 0.001.

Using this strong statistical link, the team built a regression model that explained 42.1 percent of the observed variance in precipitation over the calibration period. That may sound modest, but in paleoclimate reconstruction it represents a robust signal, particularly for a variable as noisy as precipitation. The model was then applied backward in time to generate annual precipitation estimates from 1735 to 2024. To correct for the tendency of regression models to underestimate the true range of past variability, the researchers applied variance scaling, a standard technique that adjusts the amplitude of the reconstruction to match the variance observed in the instrumental record. The final product captures both sharp year-to-year swings and slower, low-frequency oscillations in the region’s water supply.

What does three centuries of Siberian rainfall look like? The reconstruction shows that the past was anything but stable. Several persistent dry intervals and equally persistent wet episodes punctuated the record across the eighteenth, nineteenth and twentieth centuries, demonstrating that central Siberia has long experienced multi-year to multi-decadal hydroclimatic swings. Yet the most recent decades are distinctive. The record shows a marked shift toward wetter conditions in recent years, a feature that stands out against the full 290-year backdrop and suggests that the current wetting is not simply another natural fluctuation but part of a genuine, ongoing hydroclimatic transition.

To understand what might be driving this shift, the researchers turned to the large-scale ocean–atmosphere machinery that governs northern Eurasian climate. The reconstructed precipitation shows significant associations with several major modes of climate variability. North Atlantic sea-surface temperature anomalies emerged as closely related to precipitation variability at Turukhansk, consistent with growing evidence that Atlantic multidecadal variability exerts a remote influence on Siberian rainfall. Wavelet coherence analysis, a technique that reveals how the correlation between two time series changes across both time and frequency, showed that the relationships between precipitation and the Atlantic Multidecadal Oscillation, the Arctic Oscillation, and the Scandinavian pattern all vary through time and across timescales. In other words, the teleconnections that deliver moisture to central Siberia are not fixed; they strengthen, weaken and reorganize over the decades.

The most provocative finding, however, concerns the Arctic itself. The recent wetting coincides with rapid Arctic warming and with declining sea-ice concentration, and the strongest statistical relationship with sea ice occurs over the Barents–Kara Seas, the sector of the Arctic Ocean immediately north and west of the study region. This matters because sea-ice loss is a cornerstone of Arctic amplification, the phenomenon by which the Arctic warms two to four times faster than the globe as a whole. Open water releases heat and moisture to the atmosphere, alters the temperature contrast between the pole and mid-latitudes, and reshapes the jet stream and storm tracks. The study’s atmospheric circulation and moisture-transport analyses provide the mechanistic link: wetter conditions in central Siberia are accompanied by circulation anomalies that favor enhanced moisture transport toward the region and its convergence over central Siberia. Together, these lines of evidence support a close linkage between regional wetting and Arctic amplification.

The implications extend beyond the reconstruction itself. The team examined bias- and variance-adjusted projections from the CMIP6 multi-model ensemble, the latest generation of global climate models, under three future emissions scenarios: SSP1-2.6, a low-emissions pathway; SSP2-4.5, an intermediate pathway; and SSP5-8.5, a high-emissions pathway. Across all three scenarios, the models indicate that wetting in central Siberia is likely to continue through the twenty-first century, with progressively larger precipitation increases under stronger forcing. The convergence between the tree-ring evidence, the observational analyses and the model projections strengthens the case that the recent wet shift is an early expression of a longer-term trend rather than a passing anomaly.

Why should anyone outside Siberia care about more rain in one of the planet’s most sparsely populated regions? The answer lies in the outsized role the Siberian boreal zone plays in the Earth system. These forests store vast quantities of carbon, and changes in water availability influence tree growth, permafrost stability, wildfire regimes and the flux of freshwater into the Arctic Ocean. Increasing river discharge to the Arctic has already been documented, and precipitation changes over central Siberia feed directly into that flow. Wetter conditions could boost forest productivity in some areas, but they also interact with thawing permafrost, changing fire dynamics and shifting vegetation zones in ways that scientists are only beginning to untangle. A reliable long-term precipitation baseline is essential for interpreting all of these changes.

The study also demonstrates the enduring power of tree rings as climate archives. Because the spruce trees sampled near Turukhansk integrate moisture conditions over an extended window spanning two growing seasons, they capture hydroclimatic information that single-season records can miss. The 290-year reconstruction now serves as a benchmark against which both natural variability and anthropogenic trends can be measured. As the authors conclude, the record provides long-term context for the recent hydroclimatic shift in central Siberia and suggests that wetter conditions will remain an important feature of the region’s climate as warming continues. In a century defined by drought headlines across much of the world, the message from the spruce trees of central Siberia is a reminder that climate change writes very different stories in different places, and that some of the most dramatic of those stories are unfolding at the top of the planet, where melting sea ice is quite literally raining down on the forests below.

Subject of Research: A 290-year tree-ring reconstruction of precipitation in central Siberia and its link to Arctic amplification

Article Title: A 290-year tree-ring reconstruction of precipitation in central Siberia shows recent intensified wetting linked to arctic amplification

Article References: Guo, J., Bakhtiyorov, Z., Chen, F., Agafonov, L., Gurskaya, M., & Chen, Y. (2026). A 290-year tree-ring reconstruction of precipitation in central Siberia shows recent intensified wetting linked to arctic amplification. Climate Dynamics, 64(11), Article 447. https://doi.org/10.1007/s00382-026-08406-7

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08406-7

Keywords: Siberia, tree rings, precipitation reconstruction, dendrochronology, Arctic amplification, sea ice, Barents–Kara Seas, Atlantic Multidecadal Oscillation, Arctic Oscillation, CMIP6, climate dynamics, boreal forest