Guest Post by Willis Eschenbach
Part 1. The Study.
I came across a study that’s been getting some play in the usual climatastrophist circles. The study is entitled
And here’s the abstract:
Abstract
Land use changes (LUC) and global warming (GW) significantly impact the Maritime Continent’s (MC) hydro-climate, but their effects on extreme precipitation events are underexplored. … We find that LUC-induced deforestation increases surface warming, enhancing atmospheric instability and favoring local convection, leading to more frequent heavy precipitation. Meanwhile, GW amplifies the atmosphere’s water-holding capacity, further intensifying wet extremes. Our findings reveal a “wet-get-wetter, dry-get-drier” pattern driven by different mechanisms: dynamic processes primarily influence wet extremes under LUC, while changes in evapotranspiration control dry extremes. In contrast, under GW, wet extremes are driven by dynamic processes, while dry extremes are influenced by reduced moisture availability and weakened atmospheric circulation. This highlights the need for land management to address rising extreme risks.
And what is the “Maritime Continent” when it’s at home, sez I? Good question, never heard of it. Foolish me, I thought there were only seven continents. Here’s what I found out.

Figure 1. The “Maritime Continent”.
Not sure why the Solomon Islands (in gray at the lower right) isn’t included in the “Maritime Continent”. The Solos are always kind of the overlooked country in the region. I lived and worked in the Solomons for eight years, so I have some familiarity with the weather patterns. It has the same weather as the others.
In any case, the area being studied is about a third of one percent of the earth’s surface. In addition, it’s in the midst of an unusual part of the planet called the Pacific Warm Pool, with some of the highest rainfalls amounts found anywhere.

Figure 2. Average rainfall, 1979-2021. Atlantic and Pacific centered views. The red boxes mark the general area of the Maritime Continent discussed above.
Note the blue line of heavy rainfall above the equator. This is the rainfall from the semi-permanent band of thunderstorms at what’s called the “Inter Tropical Convergence Zone” (ITCZ). The ITCZ marks the boundary between the separate circulations of the northern and southern halves of the atmosphere.
Of particular interest is the large blue area in the western Pacific Ocean and the eastern Indian Ocean. This is the area of the “Pacific Warm Pool”. It’s the warmest area of the open ocean, as well as the wettest. It’s also the area chosen by the researchers for their study. For a discussion of the nature of the Pacific Warm Pool, see the post below.
The conclusion of their study is that in the Maritime Continent (which they don’t mention is only a third of a percent of the surface and is in the middle of the Pacific Warm Pool), the wet is getting wetter and the dry is getting dryer.
And how do they know this? Intensive study of the rainfall records? Analysis of patterns of rainfall? Correlation of rainfall amounts with El Nino/La Nina alterations?
Nah. That “observations” and “evidence” stuff is soooo 20th Century.
They just ran a couple of climate models, performed modern haruspicy on the entrails of the model results, and the answer popped out … modern science at its finest.
Sadly, despite the study only covering a tiny area in the middle of a unique climate region, the authors couldn’t resist declaring that we are faced with “rising extreme risks“.
Be still, my beating heart.
Part 2. The Hype
So that’s the study. Then there are the popular reports of the study, wherein it has grown markedly in the telling. There’s a typical one below. Following the unbreakable rules for such articles, the headline claims that some scientists somewhere are worried. And not just ordinary worried. Existentially worried. Ringing the bells worried. The headline says:
Scientists sound alarm after making disturbing discovery about Earth’s rainfall: ‘Urgent need’
The article, basically quoting the study’s abstract without attribution, says:
Researchers say their findings have revealed a “wet-get-wetter, dry-get-drier” pattern driven by different mechanisms. Dynamic processes largely control wet extremes under land use changes, while changes in evapotranspiration control dry extremes. However, in a warming world, dynamic processes amplify wet extremes, while a reduction in moisture and weakened atmospheric circulation influence dry extremes.
So, are they right that the wet is getting wetter and the dry is getting dryer, either in the Maritime Continent or globally? We actually have the data to determine that. A 1° latitude by 1° longitude satellite-based rainfall record since 1979 is available from Copernicus here.
Upon reflection I realized that this is actually two different questions.
• Are wet areas getting wetter and dry areas getting drier?
• Are wet times of year getting wetter and dry times of year getting drier?
Since we are considering the question of trends, let me take a slight digression. My hypothesis is that a main one of the emergent phenomena that thermoregulate the planet are the tropical thunderstorms. Thunderstorms cool the surface in a variety of ways. They keep the temperature in the Pacific Warm Pool from ever exceeding around 30° – 31°C. So per my hypothesis, the recent global warming should have been accompanied by an increase in cooling rainfall in the tropics and particularly in the area of very frequent thunderstorms, the intertropical convergence zone (ITCZ) just above the equator. Here is a look at where it’s been getting wetter and where it’s getting drier since 1979.


Figure 3. Rainfall trend. The two panels are the Pacific and Atlantic views of the same data. Red lines enclose areas which are drying at the rate of -3 mm/decade or faster. White lines enclose areas getting wetter at the rate of 3 mm/decade or more.
Note that this bears out my hypothesis, in that there is increasing thunderstorm-driven cooling in the Pacific Warm Pool and the ITCZ. But I digress into theory. Let me return to observations.
There are some fascinating and surprising things about Figure 3. The overall trend is zero. Land is drying slightly, while the ocean is growing slightly wetter. Tropical land is drying the fastest, tropical ocean is getting wetter the fastest.
Rainfall in New Zealand is decreasing. Around the equator, the largest area of decreasing rain is in between two of the largest areas of increasing rain. North America is mostly unchanged, except for some drying in the Northwest Coast. The Southern Ocean has two areas getting drier and two areas getting wetter. Most of the world’s landmass is pretty neutral, neither getting much wetter nor much dryer, except for the southern Amazon which is getting drier.
Not seeing much pattern in all of that. Well, except for the fact that the actual observations agree with my hypothesis that global warming is opposed by increasing numbers, earlier daily emergence, and greater duration (lifespans) of cooling tropical thunderstorms.
Moving on, regarding the first question about wet and dry areas, here’s a scatterplot of the decadal trend in rainfall (vertical axis) versus the average annual rainfall (horizontal axis) for each 1° latitude by 1° longitude gridcell (n = 64,800).

Figure 4. Scatterplot, rainfall trend versus average rainfall in individual 1°latitude by 1°longitude surface gridcells. Global average rainfall is ~ one meter, so “wet” and “dry” areas are based on that threshold.
As you can see in Fig. 4 above, in areas where the rainfall is less than about two meters per year, there’s no “wet gets wetter, dry gets dryer” at all. It’s only in areas of very heavy rain, more than about two meters per year, that the wet is getting wetter. Here are the areas we’re talking about.

Figure 5. The only areas of the world that are getting wetter overall are the areas where the average rainfall is over 2 meters per year..
Note that, while most of this “wet gets wetter” area is over the ocean, by a peculiar coincidence, the Maritime Continent area in the study above is also in that area.
This would tend to indicate that no matter what they discovered by aiming their models at the 0.3% of the planet that is the Maritime Continent, their conclusions are not widely applicable to the rest of the planet.
Next question is, on the Maritime Continent are the wet times of year getting wetter and the dry times of year getting drier? To investigate that, we can look at the standard deviation of the rainfall dataset. If wet gets wetter and dry gets drier, the standard deviation will increase.
So let’s start with the actual monthly rainfall on the Maritime Continent.

Figure 6. Monthly average rainfall on the Maritime Continent.
When people ask what the weather In the Solomon Islands is Iike, I say “There’s the hot wet season, followed by the hotter wetter season”. The Copernicus dataset says that is true in the Maritime Continent as well, no surprise. Note that even the driest months in the Maritime Continent record are wetter than the global average monthly rainfall of 82 mm or so.
Using my Mark I eyeball, I’m not seeing any “wet gets wetter, dry gets drier” going on. Looks like the biggest swings are in the middle of the record. But let’s look to the measurements. Here is the 10-year trailing standard deviation of the rainfall on the Maritime Continent. Each monthly data point in Figure 7 below shows the standard deviation of the previous ten years (120 months) of rainfall.

Figure 7. Ten-year trailing standard deviation of the Maritime Continent rainfall data shown in Figure 6. Each point on the yellow line shows the standard deviation of the 120 months of rainfall data prior to that time.
Again, I’m not seeing any indication of “wet gets wetter, dry gets drier”. There are changes, but no overall trend.
Finally, what’s happening with the rainfall overall? Well … nothing. Here’s the global rainfall record.

Figure 8. Monthly global average rainfall, 1979-2024. The trend of the data is 0.05 mm of increasing rainfall per decade, basically zero.
A final oddity highlighting the curious stability of the climate system is that rainfall in the northern and southern hemispheres are in opposition — even after removing the seasonal variations, when one hemisphere is wetter, the other tends to be drier, and vice versa.

Figure 9. Monthly global average rainfall (black), along with northern hemisphere rainfall (blue) and southern hemisphere rainfall (red).
And that’s what I learned about the rainfall this week. Plus now I know what the Maritime Continent is. And here in the generally dry Northern California coast, it’s raining outside my window as I write this.
What an astounding planet!
w.
It Bears Repeating: When you comment, please quote the exact words you are discussing. I choose my words with care, and I’m happy to explain and defend them. But I can’t explain or defend your restatement of what you think I meant.
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Computer models as haruspices? At least one avoids animal rights activists.
WE, there you go again, spoiling modeled alarm about a ‘maritime continent’ that does not exist with actual observational data showing no alarm at all. Well done.
Haruspicy. Thanks, my goal is to use that word at least once in the next month.
Well, you seem to have met that goal. Going to try again in April?
Madness remains on the increase. From the Conversation:
Understanding the cultural experience of keeping warm can help us embrace clean energy
Artists were appointed in each country to create artworks that highlighted various aspects of the oral histories. This included Finnish painter and textiles artist Henna Aho, Romanian photographer Denise Lobont and video artist Ram Krishna Ranjan, who lives in Sweden. I am both the project UK artist and co-ordinator of the other artists. All were selected because they had an existing interest in home heating and had experience of collaboration.
…
Some of our UK oral histories documented how coal provided people with a sense of security because they could control how long the fuel would last.
Monumentally mental.
Great stuff Willis.
A comment that is NOT a complaint..
Mollwiede projections don’t give a very good sense of just how huge the Pacific Ocean is, thus how the surface temperatures there affect the entire planet with resultant prevailing wind changes and over decades, deep current patterns, and every few years with El and La Niña. And weekly cloud cover changes. Here’s a Pacific-centric view of the planet. Try it on Google Earth yourself.
Flying across the Pacific must be a bit nerve wracking.
Sydney NSW (Australia) is further away from Southern California than northwest Europe.
Darn tootin’ – have you seen ‘Plane’ [2023]?
Tooting in South London or Tooting on Mars (Amazonis Planitia)?
I’ve been meaning to ask, any word as to when good King Richard plans to return from the Crusades and clean house in the UK?
Closest I ever got to Hawaii was when the pilot pointed it out at quite a distance on a non-stop Quantas trip from Los Angeles to Sydney. Seem to recall flying more than half a day but you could exercise walking the length of the plane and if close enough to the window watch the ocean. On the way back several islands below the new maritimes were noted, suspect that they didn’t know about the new definitions. Served good chocolates also.
All these poor uneducated guys and gals that are trying to kill history don’t do any homework so they don’t know any history. Maybe land increased from sea level there going down suddenly from the Equatorial Counter-Current totally reversing. PNAS, Science, and Nature might compete for such a paper or has it already been modeled?
Nah. Been to Japan, Singapore, and Australia literally probably a hundred times over my career. Long and boring, not nerve wracking. Nerve wracking is flying into DCA (Reagan).
Try crossing it on the oldest ship in the US Navy that keeps breaking down
… said Amelia Earhart.
True, D. Turns out that I know from interesting personal experience with the Pacific Ocean just how huge that sucker is.
In any case, your image intrigued me. So I went to Google Earth. Max eye height is 63,780 km. At that eye height I calculate you can see about 168° of the full 180° of half the sphere. So it’s slightly misleading. Check my math
> (eyedist=mi2km(39630))
[1] 63778.3
> (rad=earthradm/1000)
[1] 6371.007
> 180-asind(rad/eyedist)*2
[1] 168.5
mi2km converts miles to km.
earthradm = earth radius in meters
asind is the arc sin in degrees
Regards,
w.
“So it’s slightly misleading. Check my math.”
What’s misleading? It’s a picture, Willis.
The comment was “Mollwiede projections don’t give a very good sense of just how huge the Pacific Ocean is . . . “, which appears to be true.
i can see why you refuse to quote what other commenters say, while demanding that others do. Poor form, Willis.
Adding CO2 to air does not make the air hotter, “Steel,Greenhouse” or no. Feel free to quote my exact words.
Regarding the oxymoron “Maritime Continent” {MC} – – it isn’t working for me.
There are some things I have been able to get my head around. To name a few – jumbo shrimp, act natural, civil war, crash landing, freezer burn. MC, nawh!
Military Intelligence.
Gamble responsibly.
Very unique.
“Very unique” always makes me shout at the television.
Pedant that I am 🙁
Consider a set containing 10 Chevrolets of the same year, make and model, and an airplane.
Now, despite the industrial revolution, the 10 Chevrolets are not totally identical. in fact, every one of the ten differs in minute ways and is thus unique.
But in the set in question, 10 Chevys and an airplane, the airplane is very unique …
Similarly, every kind of animal is unique … but one of the most unique has to be the platypus, which lays eggs, has a duck’s bill, a beaver’s tail, and poison spurs … there’s nobody even remotely like that joker.
w.
“But in the set in question, 10 Chevys and an airplane, the airplane is very unique …”
Don’t be silly, Willy.
Caught one one night fishing near Glen Innes (NSW) … quite a business getting the hook out and untangling the sod, and he was quite ungrateful too!
Mr,
Yes, unique is an absolute, a binary is or is not, like pregnancy. Geoff S
John McPhee in his “Assembling California” says, “… if Australia and Africa and the Americas and Eurasia spread apart from their obvious fit, they had to have been together in the first place. The first place – according to the newly determined vectors of the lithospheric plates – was two hundred million years ago when Pangaea began to split into Laurasia and Gandwanaland.”
Two hundred million years ago was during the Triassic Period, and at least here in the video of Christopher Scortese, looks like there was a continent there that slowly pulled apart. The islands that Willis shows above certainly do look like they were conjoined at one time – a good fit.
https://www.youtube.com/watch?v=uLahVJNnoZ4
Replete with music by Taco Bell and his famous canon.
Bill: If you want to pursue the subject, you might want to look into something called the “Wallace Line”. The Wikipedia article seems pretty clear. https://en.wikipedia.org/wiki/Wallace_Line
I’m hoping that utter boredom will kill global warming.
Divination is the perfect metaphor for closed shop, secret code climate modeling. Great piece, Willis. Keep ’em coming.
I can’t find who paid for this study.
I looked also. Since all the authors are Chinese, my guess is China in a continued effort to undermine the West via climate nonsense.
Rud:
Bingo!
Plus laugh all the way to the bank since China dominates much of the renewable industrial manufacturing capacity, rare earth processing and solar panel polysilicon production.
In other words, US taxpayers paid for it. A grant from USAID, paid to the UN, handed over to the CCP propaganda/academia wing.
Just look in the Acknowledgements section: “This study was supported by the National Science and Technology Council grant 110-2628-M-002-004 MY4 to National Taiwan University.”
The National Science and Technology Council (NSTC; 國家科學及技術委員會) is a statutory agency of Executive Yuan of the Republic of China (Taiwan) for the promotion and funding of academic research, development of science and technology and science parks.
Big Rain and Big Drought both provided significant funding.
Big Pharma sat this one out.
Very nice Willis. I have a problem with this sentence.
“Meanwhile, GW amplifies the atmosphere’s water-holding capacity, further intensifying wet extremes.”
To me this implies that global warming is increasing the atmosphere’s ability to hold more water. My understanding is that global warming would cause more water vapor but the amount of water vapor the atmosphere can hold remains the same. Meaning it will rain earlier or more often. I think that sentence is misleading.
This study is useless.
The hottest air temperatures occur over the driest land. Death Valley, anyone? Or maybe the Lut Desert?
Burning fossil fuels produces, at a minimum, heat, CO2, and H2O. Where do these products go?
If measurements showed that atmospheric temperatures have increased, atmospheric CO2 and H2O levels have increased, since fossil fuel consumption increased, I wouldn’t be at all surprised!
Would anybody?
So do volcanoes
Hi Willis,
Thanks for another enlightening post, and the emphasis on using actual data, not just simulations.
In several of your figures, you show trends with units of mm/decade. Since you are mostly talking about annual rainfall amounts (but these figures do not explicitly state it), are these technically (mm/year)/decade?
Maybe I should just mention again that there around Solomon – or a bit more east – we have one of the most volcanic areas in the world. And old Eddy and others would agree with me that every 1000 years or so there is more volcanic activity on earth….
Henry, as a former Solomon resident will you please comment on this:
Why El Niños Originate from Geologic, Not Atmospheric, Sources — Plate climatology
Written by James E. Kamis on 11SEPT2015
…the 1998 and 2015 El Niños are so similar. If the atmosphere has radically changed these El Niños should be different, not absolutely identical.
In an attempt to somehow explain this giant disconnect, climate scientists have been furiously modifying their computer-generated climate models. To date the updated climate models have failed to spit out a believable explanation for this disconnect. Why? Their computer models utilize historical and current day atmospheric El Niño data. This atmospheric data is an “effect” of, and not the “cause” of El Niños.
D Sandberg,
Thank you for the Kamis reference. It helps explain why the Pacific warm pool is warm, while most research I have seen models air/land interactions.
For 20 years I have kept an eye on the literature for stories of the vertical sea temperature profiles there, to see if temperatures connect with the sea floor. My search method has been undirected, so I ask if readers have more links to this matter of submarine volcanism there. Geoff S
I agree with Kamis….
Really interesting stuff. Very much appreciated.
The answer pooped out or popped out?
“Since we are considering the question of trends, . . . “
Excellent. The only thing you get by following a trend is the knowledge that you are getting closer to an inflection point, in general.
When you write “A final oddity highlighting the curious stability of the climate system . . .”, I presume by “climate system”, you mean the complex and chaotic interactions between the atmosphere, aquasphere and lithosphere?
It seems fairly obvious that the NH and SH have the seasons occurring in opposition, due to the Earth’s orbital inclination, so the NH summer is the SH winter, etc. If the NH winter is wetter, or colder, or windier than the summer, I see no reason why the SH winter should not be likewise with respect to its summer. You can’t just “remove” seasonal variations – the season reflects the weathers. Any “adjustments” will obviously show you what you want to see.
The weather in one hemisphere should be roughly opposite to that of its brother.
Why would anybody find this surprising?
It is not possible to predict future climate states (IPCC), and adding CO2 to air does not make the air hotter (duh!).
Brilliant critique. Congratulations.
Paraphrasing Dickens character Ebenezer Scrooge in A Christmas Carol: “Models! Bah, Humbug.”
NB : People have probably moved on from this article by now, but any feedback within 48 hours (when I’ll give up checking for updates) would be greatly appreciated.
Following the link, out of curiosity, led me to the following contradiction :
NB : For this dataset it turns out “present” = “March 2024” for the monthly time resolution option.
Do the graphs actually have 2.5-degree resolution, or are they 1-degree resolution (as labelled, in which case where did you get your data from ?!?) ?
.
Re-creating an expired account and getting the dataset via their web interface gave me a 61 MB ZIP file containing 543 separate netCDF (V4 / HDF5 format) files … not exactly “user-friendly” …
Checking one extracted file gave me :
– – – – – – – – – – – – – – – – – – – – –
$ h5ls gpcp_v02r03_monthly_d202112.nc
lat_bounds Dataset {72, 2}
latitude Dataset {72}
lon_bounds Dataset {144, 2}
longitude Dataset {144}
nv Dataset {2}
precip Dataset {1, 72, 144}
precip_error Dataset {1, 72, 144}
time Dataset {1}
time_bounds Dataset {1, 2}
$
– – – – – – – – – – – – – – – – – – – – –
A 72 x 144 grid has 2.5° resolution, with “latitude” going from -88.75 (South pole) to 88.75 (North pole), and ‘longitude” going from 1.25 (°E) to 358.75 (= 1.25°W).
So far, so reasonable.
What is not “reasonable”, for me at least, is “pre-processing” 543 such files so I can check trends etc. per gridcell.
.
Trying to use their API I was unable to get the CDS website to produce a single netCDF file with all 543 monthly average “precip” values.
The python script I ended up with was :
– – – – – – – – – – – – – – – – – – – – –
import cdsapi
client = cdsapi.Client()
dataset = “satellite-precipitation”
request = {
“variable”: “all”,
“time_aggregation”: “monthly_mean”,
“year”: [
“1979”, “1980”, “1981”,
“1982”, “1983”, “1984”,
“1985”, “1986”, “1987”,
“1988”, “1989”, “1990”,
“1991”, “1992”, “1993”,
“1994”, “1995”, “1996”,
“1997”, “1998”, “1999”,
“2000”, “2001”, “2002”,
“2003”, “2004”, “2005”,
“2006”, “2007”, “2008”,
“2009”, “2010”, “2011”,
“2012”, “2013”, “2014”,
“2015”, “2016”, “2017”,
“2018”, “2019”, “2020”,
“2021”, “2022”, “2023”,
“2024”
],
“month”: [
“01”, “02”, “03”,
“04”, “05”, “06”,
“07”, “08”, “09”,
“10”, “11”, “12”
],
“data_format”: “netcdf”,
“download_format”: “unarchived”
}
target = “Precipitation_050325.nc”
client.retrieve(dataset, request, target)
– – – – – – – – – – – – – – – – – – – – –
but trying to run this — or a variant with “variable”: “precip” instead — just gave me the same ZIP file and the following run-time messages :
– – – – – – – – – – – – – – – – – – – – –
$ python3 read_Precipitation_1.python
2025-03-05 17:41:27,999 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.
2025-03-05 17:41:28,000 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the “Show API request code” tool on the dataset Download Form to check you are using the correct syntax for your API request.
2025-03-05 17:41:28,207 INFO [2022-04-01T00:00:00] Information on the current issues afflicting the data of this dataset is provided under the Known issues in the Documentation tab.
2025-03-05 17:41:28,208 INFO Request ID is …
2025-03-05 17:41:28,293 INFO status has been updated to accepted
2025-03-05 17:41:36,756 WARNING Download format not supported for this dataset. Defaulting to zip.
2025-03-05 17:41:36,757 INFO status has been updated to running
2025-03-05 17:44:20,672 INFO status has been updated to successful
$
– – – – – – – – – – – – – – – – – – – – –
Any suggestions on how to persuade CDS to give me the data as a single netCDF file ?
Hey, Mark, thanks for the followup. I fear that I have no idea how to get the data as a single file.
I first downloaded the individual files into a folder. Then I made an empty 3-D array (lat/long/month). I read each file, extracted the data, resampled it to 180°x360° (1° gridcells), and put it into a layer of the array.
This gave me a 180 x 360 x 543 array, with each layer in the array being one month. That array is about a quarter of a gigabyte in size.
Hope this helps, let me know what else I can do to assist you.
w.