bluewave

Corona, Queens is one of the prime spots for compressional heating.

The usual warm spots around the area have a shot at 104°-105° or warmer. Both the Euro and GFS have 850mb temperatures around +25 C Thursday and Friday. I believe July 2011 was the last time 850 mb temperatures reached this high.

NYC and other locations continuing the drought pattern which emerged back in September 2024. NYC hasn’t had a month with above average precipitation since May 2025. This will enhance the record heat potential around the area going into July. June 2026 MONTH TO DATE 3.39 4.41 -1.02 May 2026 NATIONAL WEATHER SERVICE NEW YORK, NY 635 PM EDT MON JUN 01 2026 ................................... ...THE CENTRAL PARK NY CLIMATE SUMMARY FOR THE MONTH OF MAY 2026... CLIMATE NORMAL PERIOD 1991 TO 2020 CLIMATE RECORD PERIOD 1869 TO 2026 WEATHER OBSERVED NORMAL DEPART LAST YEAR`S VALUE DATE(S) VALUE FROM VALUE DATE(S) NORMAL ................................................................ TEMPERATURE (F) RECORD HIGH 99 05/19/1962 LOW 32 05/06/1891 HIGHEST 93 05/19 MM MM 85 05/03 LOWEST 45 05/03 MM MM 47 05/23 AVG. MAXIMUM 71.5 71.4 0.1 69.5 AVG. MINIMUM 54.9 55.0 -0.1 55.2 MEAN 63.2 63.2 0.0 62.3 DAYS MAX >= 90 2 1.0 1.0 0 DAYS MAX <= 32 0 0.0 0.0 0 DAYS MIN <= 32 0 0.0 0.0 0 DAYS MIN <= 0 0 0.0 0.0 0 PRECIPITATION (INCHES) RECORD MAXIMUM 10.24 1989 MINIMUM 0.30 1903 TOTALS 3.05 3.96 -0.91 6.58

Based on how consistent the CFS has been, expect the Euro to increase to the +3.5 to +4 traditional ONI range also on its release coming for July 5th. This makes sense given the all -time record SST warmth we are currently experiencing so early on. Continuing WWBs will allow this event to keep intensifying at a record rate. The forecasts will be more accurate as get further past the spring predictability barrier.

I deal more in persistence rather than whether something is permanent or not. Will let history decide in the future whether something that we have been experiencing turns out to be permanent or not. Agree with you 100% that the ridges have been getting stronger over time regardless of El Niño or La Niña. But all the record warmth we have experienced in the East since March has occurred as forcing crossed the Maritime Continent. This degree forcing there at times wasn’t present when we had cooler SSTs during earlier super El Niños like 1997. Recent studies have shown more persistent forcing there as the Indio-Pacific warm pool has continued to expand. This is also why our last 2 super El Niños in 2023-2024 and 2015-2016 featured at least one winter month with forcing from the IO to WPAC which didn’t occur with past super El Niños also noted in recent papers. Forcing in those areas most of the time results in a ridge setting up over the East.

The Indian Ocean into West Pacific wasn’t anywhere near as warm back in 1972, 1982, and 1997 during those developing super El Niños. This is one of the reasons that those years didn’t feature the record warmth that we have from had from March, April, May,June, and into July in the East. The other reason is probably the record mid latitude SST warmth that didn’t exist back in those days. All of these features including the much warmer planet and drought are contributing factors. Record heat every month starting back in March with new monthly maxes tied or set in May. Newark Area, NJPeriod of record: 1843-05-01 through 2026-06-27DateHighest maximum temperatures (degrees F)Top Record 2nd Record 3rd Record 3/10 82 in 2026 81 in 2016 76 in 2006 3/11 82 in 2026 75 in 2021 71 in 1977 4/15 91 in 2026 88 in 1960 87 in 1941 5/19 99 in 2026 98 in 1962 93 in 2017+ 6/11 97 in 2026 96 in 2000 96 in 1984

The record heatwave into the 100s to start July in the East is another example of how the seasonal models don’t handle long range Maritime Continent forcing very well especially when it’s out of phase with the ENSO forcing. The long range July forecast was just focused on the developing super El Niño forcing. This has been a common theme as the more recent forecasts show much stronger Maritime Continent forcing than the seasonal long range forecast did. So just looking at the seasonal guidance you wouldn’t get any indication that there would be a 594 DM+ heat dome in the East producing record highs over 100°. So we often get multiple regions of forcing matching the locations of the warmest +30C SSTs. Old July seasonal forecast New July forecast

Record heat every month starting back in March with new monthly maxes tied or set in May. Newark Area, NJPeriod of record: 1843-05-01 through 2026-06-27DateHighest maximum temperatures (degrees F)Top Record 2nd Record 3rd Record 3/10 82 in 2026 81 in 2016 76 in 2006 3/11 82 in 2026 75 in 2021 71 in 1977 4/15 91 in 2026 88 in 1960 87 in 1941 5/19 99 in 2026 98 in 1962 93 in 2017+ 6/11 97 in 2026 96 in 2000 96 in 1984

The subtropical North Atlantic is the warmest on record for June 26th.

Makes you really miss years like 1996 with 77”-92” of snow and only 1-5 days reaching 90° on parts of Long Island. Snowfall Data for October 1, 1995 through April 30, 1996 Click column heading to sort ascending, click again to sort descending. BRIDGEHAMPTON COOP 84.0 GREENPORT POWER HOUSE COOP 80.0 ISLIP-LI MACARTHUR AP WBAN 77.1 PATCHOGUE 2 N COOP 92.0 RIVERHEAD RESEARCH FARM COOP 78.8 UPTON COOP - NWSFO NEW YORK COOP 86.2 90° Day Data for January 1, 1996 through December 31, 1996 Click column heading to sort ascending, click again to sort descending. SETAUKET STRONG COOP 5 UPTON COOP - NWSFO NEW YORK COOP 3 PATCHOGUE 2 N COOP 2 ISLIP-LI MACARTHUR AP WBAN 2 RIVERHEAD RESEARCH FARM COOP 2 BRIDGEHAMPTON COOP 1 GREENPORT POWER HOUSE COOP 1

All 3 major models are now showing 105° potential for the warmest areas this week. Just goes to show what is possible when drought feedback coincides with such a strong ridge and a warming climate. While the sample size for super El Niño events is small, this would be a first for a developing El Niño this strong. The other examples of heat approaching or exceeding 105° at the warm spots during July were La Ninas like 2011, 2010, 2001, 1966, and some years closer to neutral. So the heat is getting an early start with this super El Niño. Past super El Niño events waited until the fall through the following summers for their greatest warmth relative to the seasons.

Plenty of more rural to suburban stations outside the main I-95 urban corridor are high up on the list for most 90°days on record through the end of June from Eastern PA into New England.

The more rural observation sites are ranked just as high for the 90° day rankings as the urban ones.

Your local area not really representative of the much warmer pattern experienced around the greater region from the spring into summer so far. Parts of NJ had early record 100° heat for May. Many local climate sites are currently in the top 5 for the most 90° days by June 24. But I agree with you that the pullbacks closer to average between the record heat days are probably the developing El Nino influence. While super El Niños are a small sample size, none of the previous developing super El Niños came anywhere close to the number of 90° days so early in the season. Most of the shared top 5 years are La Niña, neutral, or weaker El Niños. Plus the warming climate is also influence increasing the 90° days counts.

Big warming north of Hawaii and with the +AMO increase off the East Coast are associated with record 100°+ heat being forecast in the East to start July. This has more in common with La Niña than a super El Niño in July. Notice how nearly all the analog dates are developing or established La Niña Summers. So while the daily PDO is closer to neutral than at this time last year, the atmospheric 500 mb pattern more resembles La Niña over North America.

We knew back in May when NJ hit 100° that we were on a much warmer summer super El Niño track than what we have seen before. The floor now looks like 102°-104° at the usual warm spots as the GFS and CMC have. The Euro is really going bonkers over 105°+. This is about as warm as we have seen all 3 major models go within a 7 day forecast.

Near to record start to the severe season in that area. Illinois has seen some of the biggest increases in tornadoes over the years. Hopefully, the models are correct and the worst storms stay north of Chicago for your vacation.

Our last two super El Niños had at least one month with dominant Maritime Continent forcing. December 2015 Maritime Continent forcing transitioned to more El Niño-like forcing in January and February. This was the strongest El Niño on record. 2023-2024 also had one winter month with Maritime Continent forcing in January.

Right now 95°-99° like the GFS and GEM looks like the floor. The ceiling is probably around 100°+ like the Euro has. Unfortunately, this will continue to dry things out.

As the globe keeps warming the +30C areas will become 31C and eventually 32 C etc…Right now once we approach 30 C, that is were the forcing tends to gravitate toward. The coming heatwave will have a combination of forcing from the Maritime Continent to the Nino regions. So at this time the more westward forcing will be driving the bus so to speak.

SSTs near +30°C will produce forcing that overlaps with the primary El Nino standing wave. Plus large areas of mid-latitude record SST warmth will add a -PDO La Niña-like influence. The coming heatwave for late June into early July is something we more have associated with strong La Niña or -PDO patterns. The analog composite and July coorelations are mostly comprised of established La Ninas or developing La Nina’s with a strong +SOI.

Yeah, the fastest El Niño development experienced during modern record keeping.

Islip has experienced 15 out of the last 22 months since the drought started in September 2024 with below average precipitation bolded below. Monthly Total Precipitation for ISLIP-LI MACARTHUR AP, NY Click column heading to sort ascending, click again to sort descending. 2026 2.58 3.66 4.19 2.15 2.68 0.66 M M M M M M 15.92 2025 0.60 3.72 4.76 1.98 4.67 1.88 5.64 0.53 1.58 5.06 2.72 3.77 36.91 2024 7.32 2.40 9.54 3.45 4.67 2.44 2.55 6.50 0.24 0.12 3.34 6.23 48.80

Another extreme heatwave matching the findings of this recent paper. Conclusion Actionable climate assessment for effective climate adaptation and mitigation requires skillful and reliable projections of extreme weather risks under different emission scenarios on a regional to local level. This holds particularly true for the representation of recently observed extremes of large magnitude that might be rare under current climatic conditions but will become more likely under continued GHG emissions (1, 56, 64). Skillful projections of trends in such “extreme-extremes” (unprecedented or record-shattering extremes) must build on a thorough physical understanding of why they are emerging and the nonlinear behavior responsible so that model simulations can be benchmarked and potential biases can be accounted for. In large and densely populated areas such as western Europe and China and other areas that feature important biomes for the world climate such as the Amazon, and polar regions around Greenland and Canada, some of which have been discussed in the context of climate tipping points (65, 66), the multimodel mean of climate simulations of the past decades does not show the enhanced warming of the temperature distributions’ upper tails observed in these regions (Fig. 1 and SI Appendix, Fig. S5). Note that for the Amazon, the strongest trends have emerged over the past 23 y and are found for ERA5 only (SI Appendix, Fig. S4). Often, the multimodel mean is used and prioritized in many assessments of climate risks, while upper percentiles are treated as implausible scenarios and are at times rejected as outliers. For instance, the 1.5 °C warming target established by the Paris Agreement was set largely based on avoiding “dangerous climate change,” in part associated with critical tipping elements and/or thresholds in the Earth system (65, 67). However, if impacts of global warming, such as amplified extreme heat, proceed faster than expected based on the multimodel mean projections used to support such a warming target, its utility may deserve reconsideration. We find that in numerous regions (Figs. 2 and 3), trends in the tail-widening of extreme heat distribution over the past 65 y exceed the 95th percentile of the model spread and, in some cases, even exceed the spread entirely. Trends shown in ERA5 reanalysis are outside of the modeled range for southern South America, the Arabian Peninsula, and Arctic Canada (Fig. 3 D, E, and H), irrespective of any model configuration investigated here, while the observed uncertainty intervals determined by bootstrapping overlap with the model spread. These findings hold for model simulations at higher resolution, or forced with historical SSTs, as well as with greatly expanded ensemble sizes (SI Appendix, Figs. S5, S8, and S9). Newer modeling initiatives such as super-high-resolution frameworks suggested, e.g., in the Earth Virtualization Engine (EVE) (68) promise convection permitting resolution and may offer possibilities in improving the depiction of important mechanisms. However, no substantial improvement for the higher resolved subset of the investigated models was found. Super-high-resolution, convection-resolving models may better represent processes that link SSTs with Rossby waves and associated extremes (45), regional blocking, and realistic surface response of heat events to such atmospheric patterns (50, 51). However, limitations due to data storage and computing costs might be significant constraints for the study of extreme events with high-resolution modeling frameworks, as the long time series lengths and large ensemble sizes needed for adequate statistics and trend attribution may be too resource intensive and not readily available. Newer generation models have also shown an improved skill in modeling blocking events which is more pronounced in high-resolution models (47, 69). Given the importance of nonlinear feedbacks involving hydroclimatic processes, a proper representation of the seasonal relationships of the flow of energy and water in the soil–vegetation–atmosphere continuum needs to be assured (7). Reasonable forecasts of past extreme heatwaves suggest that models can in principle produce such extreme-extremes when directly forced with the correct boundary conditions (11, 70). Ensemble boosting techniques can be used to create large ensembles of extraordinary extremes at reduced computational cost (71, 72). In an evolutionary manner, these algorithms preserve those that follow an extreme trajectory while filtering out others. This allows a sampling around a specific event characteristic. A large ensemble of highly anomalous events, which would be featured only at an extremely low rate in large ensembles (20), allows for an in-depth and statistically robust analysis of the governing physics of extreme-extremes in models. However, disentangling the relative importance of externally forced and internal variability in the observed trends may be key to attributing the sources of model–observation discrepancies. Coordinated single forcing large ensemble experiments such as the new Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP) (73) might help in improving our understanding in the relative role of various external or internal drivers in extreme event trends. Further, machine learning (ML) approaches have shown promising results for providing more reliable bias adjustment of climate model output. These are based on methods from image processing and are better in retaining the relationships between variables compared to more traditional quantile-mapping approaches. This is particularly important when analyzing risks and impacts from compound extremes. ML techniques could also assist in detecting nonlinear and regime-changing behavior in the ocean–atmosphere–land–vegetation system and provide causality where common drivers experience strong coupling and feedbacks (9, 74). Beyond using ML for analysis, recent advances in ML-driven weather forecasts exemplify its potential in climate modeling (75, 76). In addition, ML might offer accurate and less computationally costly solutions for resolving important subgrid processes (77, 78), compared to purely numerical frameworks. ML approaches, however, must be combined with others that can physically explain and understand the causal flows identified by ML. New assimilation techniques that integrate observational datasets and exploit advanced interpolation frameworks have been proven to improve the depiction of extremes compared to reanalysis datasets (79) and provide climate information at a higher resolution. While our findings provide many avenues for interesting and relevant new research, the authors stress that the best way to reduce both uncertainty in and exposure to climate impacts is a rapid transition of relevant societal sectors away from fossil fuels to stabilize global temperature rise.